A1 A2 A3
Yao-Chih Hsu,
references
data download from https://freeway2024.tw/.
dataset input
library(readxl)
A1_112_1_10 <- read_xlsx(path = "112年1-10月A1事故資料(113.01.12更新).xlsx")
A2_112_1_10 <- read_xlsx(path = "112年1-10月A2事故資料(113.02.06更新).xlsx")
A3_112_1_10 <- read_xlsx(path = "112年1-10月A3事故資料(113.02.06更新).xlsx")
A1A2A3_113_1_2 <- read_xlsx(path = "113年1-2月A1A2A3交通事故資料.xlsx")| # 112 Jan. to Oct. A1A2A3 accidents (A1A2A3_112_1_10) # data |
|---|
| # 112 Jan. to Oct. A1 accidents (A1_112_1_10) # summary |
r library(dplyr) summary(A1_112_1_10) |
| ``` 年 月 日 時 分 Min. :2023 Min. : 1.000 Min. : 2.00 Length:45 Length:45 1st Qu.:2023 1st Qu.: 4.000 1st Qu.: 8.00 Class :character Class :character Median :2023 Median : 5.000 Median :19.00 Mode :character Mode :character Mean :2023 Mean : 5.533 Mean :16.22 3rd Qu.:2023 3rd Qu.: 8.000 3rd Qu.:21.00 Max. :2023 Max. :10.000 Max. :30.00 秒 事故類別 縣市 市區鄉鎮 路線 Length:45 Length:45 Length:45 Length:45 Length:45 Class :character Class :character Class :character Class :character Class :character Mode :character Mode :character Mode :character Mode :character Mode :character |
| 公里 公尺 里程 分局 工務段 Min. : 1.0 Min. : 0.0 Min. : 1.8 Length:45 Length:45 1st Qu.: 51.0 1st Qu.:100.0 1st Qu.: 51.9 Class :character Class :character Median :111.0 Median :400.0 Median :111.1 Mode :character Mode :character Mean :136.4 Mean :408.9 Mean :136.8 3rd Qu.:200.0 3rd Qu.:700.0 3rd Qu.:200.1 Max. :414.0 Max. :900.0 Max. :414.3 向 車道線(側)名稱 24小時內死亡人數 2-30日內死亡人數 受傷 Length:45 Length:45 Min. :1.000 Min. :0 Min. : 0.000 Class :character Class :character 1st Qu.:1.000 1st Qu.:0 1st Qu.: 0.000 Mode :character Mode :character Median :1.000 Median :0 Median : 0.000 Mean :1.178 Mean :0 Mean : 1.578 3rd Qu.:1.000 3rd Qu.:0 3rd Qu.: 1.000 Max. :4.000 Max. :0 Max. :22.000 天候 道路照明設備(11207新增) 道路類別 速限 道路型態 Length:45 Length:45 Length:45 Min. : 40.00 Length:45 Class :character Class :character Class :character 1st Qu.: 90.00 Class :character Mode :character Mode :character Mode :character Median :100.00 Mode :character Mean : 92.89 3rd Qu.:110.00 Max. :110.00 事故位置 路面狀況-路面鋪裝 路面狀況-路面狀態 路面狀況-路面缺陷 道路障礙-障礙物 Length:45 Length:45 Length:45 Length:45 Length:45 Class :character Class :character Class :character Class :character Class :character Mode :character Mode :character Mode :character Mode :character Mode :character |
| 道路障礙-視距 號誌-號誌種類 號誌-號誌動作 車道劃分設施-分向設施 Length:45 Length:45 Length:45 Length:45 Class :character Class :character Class :character Class :character Mode :character Mode :character Mode :character Mode :character |
| 車道劃分設施-分道設施-快車道或一般車道間 車道劃分設施-分道設施-快慢車道間 Length:45 Length:45 Class :character Class :character Mode :character Mode :character |
| 車道劃分設施-分道設施-路面邊線 事故類型及型態代碼 事故類型及型態2 當事者順位 當事者屬(性)別 Length:45 Min. : 5.00 Length:45 Min. :1 Length:45 Class :character 1st Qu.:13.00 Class :character 1st Qu.:1 Class :character Mode :character Median :16.00 Mode :character Median :1 Mode :character Mean :16.07 Mean :1 3rd Qu.:20.00 3rd Qu.:1 Max. :29.00 Max. :1 當事者事故發生時年齡 受傷程度 保護裝備 行動電話、電腦或其他相類功能裝置名稱 Min. :20.00 Length:45 Length:45 Length:45 1st Qu.:33.00 Class :character Class :character Class :character Median :44.00 Mode :character Mode :character Mode :character Mean :43.98 3rd Qu.:51.00 Max. :83.00 當事者區分(大類別) 當事者區分(類別) 車輛用途 飲酒情形名稱 初步分析研判子類別-主要 Length:45 Length:45 Length:45 Length:45 Length:45 Class :character Class :character Class :character Class :character Class :character Mode :character Mode :character Mode :character Mode :character Mode :character |
| 肇事逃逸(是否肇逃) Length:45 Class :character Mode :character |
| ``` |
r n <- ncol(A1_112_1_10) A1_112_1_10.unique <- sapply(1:n, function(x){A1_112_1_10[ ,x] %>% unique() %>% unlist() %>% unname() %>% sort() %>% return()}) names(A1_112_1_10.unique) = colnames(A1_112_1_10) print(A1_112_1_10.unique) |
| ``` $年 [1] 2023 |
| $月 [1] 1 2 3 4 5 6 7 8 9 10 |
| $日 [1] 2 4 6 7 8 10 12 13 15 16 18 19 20 21 22 24 25 26 28 30 |
| $時 [1] “01” “02” “03” “04” “05” “06” “07” “08” “09” “10” “12” “13” “14” “15” “16” “17” “19” “20” “21” [20] “23” |
| $分 [1] “00” “01” “02” “03” “04” “05” “07” “08” “10” “11” “12” “15” “19” “21” “25” “28” “29” “35” “39” [20] “40” “41” “42” “43” “44” “46” “47” “48” “50” “54” “55” “57” “58” “59” |
| $秒 [1] “00” “03” “05” “06” “07” “10” “11” “20” “21” “28” “31” “32” “35” “36” “40” “41” “42” “48” “49” [20] “55” |
| $事故類別 [1] “A1” |
| $縣市 [1] “南投縣” “屏東縣” “苗栗縣” “桃園市” “高雄市” “基隆市” “雲林縣” “新北市” “新竹縣” “彰化縣” “臺中市” [12] “臺北市” “臺南市” |
| $市區鄉鎮 [1] “七堵區” “大園區” “中和區” “中壢區” “五股區” “內湖區” “斗六市” “白河區” “竹山鎮” “竹北市” “竹田鄉” [12] “竹南鎮” “秀水鄉” “芎林鄉” “岡山區” “林口區” “花壇鄉” “南屯區” “後龍鎮” “苑裡鎮” “桃園區” “通霄鎮” [23] “造橋鄉” “善化區” “湖口鄉” “新店區” “新營區” “楊梅區” “彰化市” “旗山區” “潭子區” “頭屋鄉” “霧峰區” [34] “寶山鄉” “蘆竹區” “鶯歌區” |
| $路線 [1] “國道10號” “國道1號” “國道2號” “國道3號” “國道4號” “國道6號” |
| $公里 [1] 1 4 6 12 17 23 27 32 36 38 51 53 61 67 73 85 87 89 106 111 123 126 127 128 [25] 141 157 181 199 200 204 244 263 288 305 312 342 346 349 414 |
| $公尺 [1] 0 100 200 300 400 500 600 700 800 900 |
| $里程 [1] 1.8 4.0 6.8 12.6 17.4 23.0 27.1 27.7 32.0 36.0 38.8 51.9 53.0 61.9 67.9 73.9 [17] 85.9 87.2 87.6 89.6 106.4 111.1 123.5 126.2 127.2 128.0 128.6 141.0 157.0 181.0 181.6 199.9 [33] 200.1 200.4 204.1 244.3 263.7 288.7 305.7 312.3 342.1 346.1 349.4 414.3 |
| $分局 [1] “中分局” “北分局” “南分局” |
| $工務段 [1] “大甲工務段” “中壢工務段” “內湖工務段” “斗南工務段” “木柵工務段” “白河工務段” “岡山工務段” [8] “南投工務段” “屏東工務段” “苗栗工務段” “新營工務段” “關西工務段” |
| $向 [1] “北側” “西側” “東側” “南側” |
$車道線(側)名稱 [1] “中內” “中線” “內側”
“外側” “交流道” “爬坡” [7] “苑裡出口匝道” “路肩” “輔助” “槽化線” |
$24小時內死亡人數 [1] 1 2 4 |
$2-30日內死亡人數 [1] 0 |
| $受傷 [1] 0 1 2 3 4 6 7 22 |
| $天候 [1] “雨” “陰” “晴” “暴雨” |
$道路照明設備(11207新增) [1]
“日間自然光線” “有照明且開啟” [3] “有照明未開啟或故障”
“夜間(或隧道、地下道、涵洞)有照明” [5]
“夜間(或隧道、地下道、涵洞)無照明” “晨或暮光” [7] “無照明” |
| $道路類別 [1] “其他” “國道” |
| $速限 [1] 40 50 90 100 110 |
| $道路型態 [1] “其他” “直路” “高架道路” “彎曲路及附近” |
| $事故位置 [1] “加速車道” “快車道” “其他” “直路” “直線匝道” “路肩、路緣” [7] “環道匝道” “彎曲路及附近” |
$路面狀況-路面鋪裝 [1] “快車道” “柏油”
“路肩、路緣” “環道匝道” |
$路面狀況-路面狀態 [1] “乾燥” “濕潤” |
$路面狀況-路面缺陷 [1] “無缺陷” |
$道路障礙-障礙物 [1] “無障礙物” |
$道路障礙-視距 [1] “良好” |
$號誌-號誌種類 [1] “無號誌” |
$號誌-號誌動作 [1] “無號誌” |
$車道劃分設施-分向設施 [1] “無分向設施”
“寬式(50公分以上)” “寬式附柵欄” |
$車道劃分設施-分道設施-快車道或一般車道間
[1] “未繪設車道線” “車道線(附標記)” |
$車道劃分設施-分道設施-快慢車道間 [1]
“未繪設快慢車道分隔線” |
$車道劃分設施-分道設施-路面邊線 [1]
“有” |
| $事故類型及型態代碼 [1] 5 9 10 12 13 16 17 18 20 23 29 |
| $事故類型及型態2 [1] “同向擦撞” “在路上作業中” “其他” “追撞” “側撞” [6] “路上翻車、摔倒” “對撞” “撞交通島” “撞護欄(樁)” |
| $當事者順位 [1] 1 |
$當事者屬(性)別 [1] “女” “男” |
| $當事者事故發生時年齡 [1] 20 25 26 28 30 31 33 34 35 36 38 41 43 44 45 46 47 48 49 51 52 53 58 60 62 63 67 69 83 |
| $受傷程度 [1] “24小時內死亡” “未受傷” “受傷” |
| $保護裝備 [1] “不明” “未戴安全帽或未繫安全帶(未使用幼童安全椅)” [3] “未戴案全帽或未繫安全帶(未使用幼童安全椅)” “其他(無需使用保護裝備之人)” [5] “戴半寬式安全帽” “戴安全帽或繫安全帶(使用幼童安全椅)” [7] “繫安全帶(使用幼童安全椅)” |
$行動電話、電腦或其他相類功能裝置名稱 [1]
“不明” “未使用” “非駕駛人” |
$當事者區分(大類別) [1] “人” “大客車”
“大貨車” “小客車” [5] “小客車(含客、貨兩用)” “小貨車(含客、貨兩用)”
“半聯結車” “全聯結車” [9] “曳引車” “機車” |
$當事者區分(類別) [1] “自用” “行人”
“計程車” “租賃車” “普通重型” “遊覽車” “營業用” |
| $車輛用途 [1] “其他” “非駕駛人及乘客” “砂石車” |
| $飲酒情形名稱 [1] “不明” “呼氣檢測 0.80 mg/L以上或血液檢測 0.16%以上” [3] “非駕駛人，未檢測” “無法檢測” [5] “經呼氣檢測0.260.40mg/L或血液檢測0.051%0.08%” “經呼氣檢測未超過0.15mg/L或血液檢測未超過0.03%” [7] “經檢測無酒精反應” |
$初步分析研判子類別-主要 [1]
“未依規定減速” “未注意車前狀態” [3] “未保持行車安全距離”
“車輛或機械操作不當(慎)” [5] “其他不當駕車行為”
“其他引起事故之疏失或行為” [7] “其他引起事故之違規或不當行為”
“恍神、緊張、心不在焉分心駕駛” [9] “疲勞(患病)駕駛失控” “酒醉(後)駕駛”
[11] “酒醉(後)駕駛失控” “患病或服用藥物(疲勞)駕駛” [13] “超速失控”
“飲食、抽(點)菸、拿(撿)物品分心駕駛” [15] “違反禁行車種標誌(字)”
“違規停車或暫停不當而肇事” [17] “變換車道不當” “變換車道或方向不當” |
$肇事逃逸(是否肇逃) [1] “否” ``` |
r summary(A1_112_1_10.unique) |
Length Class Mode 年 1 -none- numeric 月 10 -none- numeric 日 20 -none- numeric 時 20 -none- character 分 33 -none- character 秒 20 -none- character 事故類別 1 -none- character 縣市 13 -none- character 市區鄉鎮 36 -none- character 路線 6 -none- character 公里 39 -none- numeric 公尺 10 -none- numeric 里程 44 -none- numeric 分局 3 -none- character 工務段 12 -none- character 向 4 -none- character 車道線(側)名稱 10 -none- character 24小時內死亡人數 3 -none- numeric 2-30日內死亡人數 1 -none- numeric 受傷 8 -none- numeric 天候 4 -none- character 道路照明設備(11207新增) 7 -none- character 道路類別 2 -none- character 速限 5 -none- numeric 道路型態 4 -none- character 事故位置 8 -none- character 路面狀況-路面鋪裝 4 -none- character 路面狀況-路面狀態 2 -none- character 路面狀況-路面缺陷 1 -none- character 道路障礙-障礙物 1 -none- character 道路障礙-視距 1 -none- character 號誌-號誌種類 1 -none- character 號誌-號誌動作 1 -none- character 車道劃分設施-分向設施 3 -none- character 車道劃分設施-分道設施-快車道或一般車道間 2 -none- character 車道劃分設施-分道設施-快慢車道間 1 -none- character 車道劃分設施-分道設施-路面邊線 1 -none- character 事故類型及型態代碼 11 -none- numeric 事故類型及型態2 9 -none- character 當事者順位 1 -none- numeric 當事者屬(性)別 2 -none- character 當事者事故發生時年齡 29 -none- numeric 受傷程度 3 -none- character 保護裝備 7 -none- character 行動電話、電腦或其他相類功能裝置名稱 3 -none- character 當事者區分(大類別) 10 -none- character 當事者區分(類別) 7 -none- character 車輛用途 3 -none- character 飲酒情形名稱 7 -none- character 初步分析研判子類別-主要 18 -none- character 肇事逃逸(是否肇逃) 1 -none- character |
r print(paste0("There have ", sum(is.na(A1_112_1_10)), " NA(s) in this dataset.")) |
[1] "There have 7 NA(s) in this dataset." |
| # data plot |
r # all A1_112_1_10.plot <- A1_112_1_10 name <- names(A1_112_1_10.unique) for (i in 1:ncol(A1_112_1_10.plot)) { barplot(table(A1_112_1_10.plot[ ,i]), main = name[i], xlab = "類別", ylab = "頻率", las = 2) } |
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112 Jan. to Oct. A2 accidents (A2_112_1_10)
summary
library(dplyr)
summary(A2_112_1_10) 年 月 日 時 分
Min. :2023 Min. : 1.000 Min. : 1.00 Length:3451 Length:3451
1st Qu.:2023 1st Qu.: 4.000 1st Qu.: 8.00 Class :character Class :character
Median :2023 Median : 7.000 Median :15.00 Mode :character Mode :character
Mean :2023 Mean : 6.595 Mean :15.39
3rd Qu.:2023 3rd Qu.: 9.000 3rd Qu.:23.00
Max. :2023 Max. :12.000 Max. :31.00
秒 事故類別 縣市 市區鄉鎮 路線
Length:3451 Length:3451 Length:3451 Length:3451 Length:3451
Class :character Class :character Class :character Class :character Class :character
Mode :character Mode :character Mode :character Mode :character Mode :character
公里 公尺 里程 分局 工務段
Min. : 0.0 Min. : 0.0 Min. : 0.0 Length:3451 Length:3451
1st Qu.: 34.0 1st Qu.:100.0 1st Qu.: 34.3 Class :character Class :character
Median : 79.0 Median :400.0 Median : 79.3 Mode :character Mode :character
Mean :135.0 Mean :411.4 Mean :135.4
3rd Qu.:232.5 3rd Qu.:700.0 3rd Qu.:232.9
Max. :426.0 Max. :950.0 Max. :426.0
向 車道線(側)名稱 24小時內死亡人數 2-30日內死亡人數 受傷
Length:3451 Length:3451 Min. :0 Min. :0.000000 Min. : 0.00
Class :character Class :character 1st Qu.:0 1st Qu.:0.000000 1st Qu.: 1.00
Mode :character Mode :character Median :0 Median :0.000000 Median : 1.00
Mean :0 Mean :0.003767 Mean : 1.64
3rd Qu.:0 3rd Qu.:0.000000 3rd Qu.: 2.00
Max. :0 Max. :2.000000 Max. :13.00
天候 道路照明設備(11207新增) 道路類別 速限 道路型態
Length:3451 Length:3451 Length:3451 Min. : 0.00 Length:3451
Class :character Class :character Class :character 1st Qu.: 90.00 Class :character
Mode :character Mode :character Mode :character Median :100.00 Mode :character
Mean : 94.29
3rd Qu.:110.00
Max. :110.00
事故位置 路面狀況-路面鋪裝 路面狀況-路面狀態 路面狀況-路面缺陷 道路障礙-障礙物
Length:3451 Length:3451 Length:3451 Length:3451 Length:3451
Class :character Class :character Class :character Class :character Class :character
Mode :character Mode :character Mode :character Mode :character Mode :character
道路障礙-視距 號誌-號誌種類 號誌-號誌動作 車道劃分設施-分向設施
Length:3451 Length:3451 Length:3451 Length:3451
Class :character Class :character Class :character Class :character
Mode :character Mode :character Mode :character Mode :character
車道劃分設施-分道設施-快車道或一般車道間 車道劃分設施-分道設施-快慢車道間
Length:3451 Length:3451
Class :character Class :character
Mode :character Mode :character
車道劃分設施-分道設施-路面邊線 事故類型及型態代碼 事故類型及型態2 當事者順位 當事者屬(性)別
Length:3451 Min. : 3.00 Length:3451 Min. :1 Length:3451
Class :character 1st Qu.:13.00 Class :character 1st Qu.:1 Class :character
Mode :character Median :13.00 Mode :character Median :1 Mode :character
Mean :14.34 Mean :1
3rd Qu.:13.00 3rd Qu.:1
Max. :30.00 Max. :1
當事者事故發生時年齡 受傷程度 保護裝備 行動電話、電腦或其他相類功能裝置名稱
Min. : -1.00 Length:3451 Length:3451 Length:3451
1st Qu.: 29.00 Class :character Class :character Class :character
Median : 38.00 Mode :character Mode :character Mode :character
Mean : 39.62
3rd Qu.: 50.00
Max. :103.00
當事者區分(大類別) 當事者區分(類別) 車輛用途 飲酒情形名稱 初步分析研判子類別-主要
Length:3451 Length:3451 Length:3451 Length:3451 Length:3451
Class :character Class :character Class :character Class :character Class :character
Mode :character Mode :character Mode :character Mode :character Mode :character
肇事逃逸(是否肇逃)
Length:3451
Class :character
Mode :character
n <- ncol(A2_112_1_10)
A2_112_1_10.unique <- sapply(1:n, function(x){A2_112_1_10[ ,x] %>% unique() %>% unlist() %>% unname() %>% sort() %>% return()})
names(A2_112_1_10.unique) = colnames(A2_112_1_10)
print(A2_112_1_10.unique)$年
[1] 2023
$月
[1] 1 2 3 4 5 6 7 8 9 10 11 12
$日
[1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31
$時
[1] "00" "01" "02" "03" "04" "05" "06" "07" "08" "09" "10" "11" "12" "13" "14" "15" "16" "17" "18"
[20] "19" "20" "21" "22" "23"
$分
[1] "00" "01" "02" "03" "04" "05" "06" "07" "08" "09" "10" "11" "12" "13" "14" "15" "16" "17" "18"
[20] "19" "20" "21" "22" "23" "24" "25" "26" "27" "28" "29" "30" "31" "32" "33" "34" "35" "36" "37"
[39] "38" "39" "40" "41" "42" "43" "44" "45" "46" "47" "48" "49" "50" "51" "52" "53" "54" "55" "56"
[58] "57" "58" "59"
$秒
[1] "00" "01" "02" "03" "04" "05" "06" "07" "08" "09" "10" "11" "12" "13" "14" "15" "16" "17" "18"
[20] "19" "20" "21" "22" "23" "24" "25" "26" "27" "28" "29" "30" "31" "32" "33" "34" "35" "36" "37"
[39] "38" "39" "40" "41" "42" "43" "44" "45" "46" "47" "48" "49" "50" "51" "52" "53" "54" "55" "56"
[58] "57" "58" "59"
$事故類別
[1] "A2"
$縣市
[1] "宜蘭縣" "南投縣" "屏東縣" "苗栗縣" "桃園市" "高雄市" "基隆市" "雲林縣" "新北市" "新竹市" "新竹縣"
[12] "嘉義市" "嘉義縣" "彰化縣" "臺中市" "臺北市" "臺南市"
$市區鄉鎮
[1] "七堵區" "九如鄉" "八德區" "三民區" "三重區" "三峽區" "三義鄉" "下營區" "土城區" "大甲區"
[11] "大同區" "大村鄉" "大肚區" "大林鎮" "大社區" "大埤鄉" "大雅區" "大園區" "大溪區" "大樹區"
[21] "中山區" "中和區" "中埔鄉" "中寮鄉" "中壢區" "五股區" "五結鄉" "仁武區" "仁德區" "內湖區"
[31] "公館鄉" "六甲區" "太保市" "文山區" "斗六市" "斗南鎮" "水上鄉" "冬山鄉" "古坑鄉" "外埔區"
[41] "左營區" "平鎮區" "民雄鄉" "永康區" "永靖鄉" "田尾鄉" "田寮區" "白河區" "石碇區" "名間鄉"
[51] "后里區" "安定區" "安南區" "安樂區" "汐止區" "竹山鎮" "竹北市" "竹田鄉" "竹東鎮" "竹南鎮"
[61] "竹崎鄉" "西屯區" "西區" "西港區" "西湖鄉" "西螺鎮" "壯圍鄉" "沙鹿區" "秀水鄉" "芎林鄉"
[71] "里港鄉" "和美鎮" "坪林區" "官田區" "宜蘭市" "岡山區" "東山區" "東區" "松山區" "林口區"
[81] "林內鄉" "芬園鄉" "花壇鄉" "虎尾鎮" "長治鄉" "阿蓮區" "前鎮區" "南屯區" "南州鄉" "南投市"
[91] "南港區" "後壁區" "後龍鎮" "柳營區" "苑裡鎮" "苓雅區" "苗栗市" "香山區" "埔里鎮" "埔鹽鄉"
[101] "崁頂鄉" "桃園區" "泰山區" "烏日區" "神岡區" "草屯鎮" "國姓鄉" "埤頭鄉" "梅山鄉" "清水區"
[111] "通霄鎮" "造橋鄉" "鳥松區" "鹿草鄉" "麻豆區" "善化區" "湖口鄉" "新化區" "新市區" "新店區"
[121] "新營區" "新豐鄉" "楊梅區" "楠梓區" "溪口鄉" "溪州鄉" "溪湖鎮" "路竹區" "彰化市" "旗山區"
[131] "銅鑼鄉" "鳳山區" "潭子區" "潮州鎮" "樹林區" "橋頭區" "燕巢區" "頭份市" "頭城鎮" "頭屋鄉"
[141] "龍井區" "龍崎區" "龍潭區" "龜山區" "礁溪鄉" "豐原區" "羅東鎮" "關西鎮" "關廟區" "霧峰區"
[151] "寶山鄉" "蘆竹區" "蘇澳鎮" "鶯歌區" "麟洛鄉" "鹽水區" "鹽埔鄉"
$路線
[1] "南港連絡道線" "國道10號" "國道1號" "國道2號" "國道3甲" "國道3號"
[7] "國道4號" "國道5號" "國道6號" "國道8號"
$公里
[1] 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23
[25] 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47
[49] 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71
[73] 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95
[97] 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 113 114 115 116 117 118 119 120
[121] 121 122 123 124 125 126 127 128 129 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145
[145] 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169
[169] 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193
[193] 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217
[217] 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241
[241] 242 243 244 245 246 247 248 249 250 251 253 254 255 256 257 258 259 260 261 262 263 264 265 266
[265] 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 291
[289] 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315
[313] 316 317 318 319 320 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340
[337] 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364
[361] 365 366 367 368 369 370 371 372 373 374 376 378 379 380 381 382 383 384 387 388 390 392 394 396
[385] 397 398 399 400 403 404 405 407 408 411 413 414 415 417 419 421 422 423 424 426
$公尺
[1] 0 4 100 200 300 400 500 600 700 750 800 900 950
$里程
[1] 0.000 0.100 0.200 0.300 0.400 0.500 0.600 0.700 0.800 0.900 1.000 1.100
[13] 1.200 1.300 1.400 1.500 1.600 1.700 1.800 1.900 2.000 2.100 2.200 2.300
[25] 2.400 2.500 2.600 2.700 2.800 2.900 3.000 3.200 3.300 3.400 3.500 3.600
[37] 3.700 3.800 4.000 4.300 4.400 4.500 4.600 4.700 4.800 4.900 5.000 5.100
[49] 5.200 5.300 5.400 5.500 5.600 5.700 5.800 5.900 5.950 6.000 6.100 6.200
[61] 6.300 6.400 6.500 6.600 6.900 7.000 7.100 7.200 7.400 7.500 7.600 7.800
[73] 7.900 8.000 8.004 8.100 8.200 8.400 8.600 8.700 8.800 8.900 9.000 9.100
[85] 9.200 9.300 9.400 9.500 9.700 9.800 9.900 10.000 10.100 10.200 10.300 10.500
[97] 10.600 10.700 10.800 10.900 11.000 11.100 11.200 11.300 11.500 11.600 11.700 11.800
[109] 11.900 12.000 12.100 12.200 12.300 12.500 12.800 12.900 13.000 13.100 13.200 13.300
[121] 13.500 13.600 13.700 13.800 13.900 14.000 14.100 14.200 14.400 14.500 14.600 14.700
[133] 14.900 15.000 15.100 15.200 15.300 15.400 15.500 15.600 15.800 15.900 16.000 16.100
[145] 16.200 16.300 16.400 16.500 16.600 16.700 16.800 16.900 17.000 17.100 17.200 17.300
[157] 17.400 17.500 17.600 17.700 17.800 17.900 18.000 18.100 18.200 18.300 18.400 18.500
[169] 18.600 18.900 19.000 19.100 19.200 19.300 19.400 19.500 19.600 19.800 19.900 20.000
[181] 20.100 20.200 20.300 20.400 20.600 20.700 20.900 21.000 21.100 21.300 21.500 21.600
[193] 21.700 21.800 21.900 22.000 22.100 22.200 22.300 22.400 22.500 22.700 22.800 23.000
[205] 23.100 23.300 23.400 23.500 23.600 23.700 23.800 23.900 24.000 24.400 24.500 24.600
[217] 24.700 24.800 24.900 25.000 25.100 25.200 25.300 25.400 25.500 25.600 25.700 25.800
[229] 25.900 26.000 26.100 26.300 26.500 26.600 26.700 26.800 26.900 27.000 27.100 27.200
[241] 27.300 27.400 27.500 27.600 27.700 27.800 27.900 28.000 28.100 28.200 28.300 28.400
[253] 28.500 28.600 28.700 28.800 28.900 29.100 29.200 29.400 29.500 29.600 29.700 29.800
[265] 29.900 30.000 30.100 30.200 30.300 30.400 30.500 30.600 30.700 30.800 30.900 31.000
[277] 31.100 31.200 31.300 31.400 31.500 31.600 31.700 31.800 31.900 32.000 32.100 32.300
[289] 32.400 32.500 32.700 32.800 32.900 33.000 33.100 33.200 33.300 33.400 33.500 33.600
[301] 33.700 33.800 33.900 34.000 34.200 34.300 34.400 34.500 34.600 34.700 34.800 34.900
[313] 35.000 35.100 35.200 35.300 35.500 35.600 35.700 35.800 35.900 36.000 36.100 36.200
[325] 36.300 36.400 36.500 36.600 36.700 36.800 36.900 37.000 37.100 37.200 37.300 37.400
[337] 37.500 37.600 37.700 37.800 37.900 38.000 38.200 38.300 38.400 38.500 38.600 38.700
[349] 38.800 38.900 39.000 39.100 39.200 39.300 39.400 39.600 39.700 39.800 40.000 40.100
[361] 40.200 40.300 40.400 40.500 40.600 40.700 40.800 40.900 41.000 41.100 41.200 41.300
[373] 41.400 41.600 41.700 41.800 41.900 42.000 42.100 42.300 42.400 42.500 42.600 42.700
[385] 42.800 42.900 43.000 43.100 43.200 43.300 43.400 43.500 43.600 43.700 43.800 43.900
[397] 44.000 44.100 44.200 44.300 44.400 44.500 44.600 44.700 44.800 45.000 45.100 45.200
[409] 45.300 45.700 45.800 45.900 46.000 46.100 46.200 46.300 46.400 46.500 46.600 46.700
[421] 46.800 46.900 47.000 47.100 47.200 47.300 47.400 47.600 47.800 48.100 48.200 48.400
[433] 48.500 48.600 48.700 48.800 48.900 49.000 49.200 49.300 49.400 49.500 49.600 49.700
[445] 49.800 49.900 50.000 50.100 50.200 50.300 50.400 50.500 50.600 50.800 50.900 51.000
[457] 51.100 51.200 51.300 51.400 51.500 51.600 51.700 51.800 51.900 52.000 52.100 52.200
[469] 52.300 52.400 52.500 52.600 52.700 52.800 52.900 53.000 53.200 53.300 53.400 53.500
[481] 53.700 53.800 53.900 54.000 54.100 54.200 54.300 54.400 54.600 54.700 54.800 54.900
[493] 55.000 55.100 55.400 55.600 55.700 55.800 55.900 56.000 56.100 56.200 56.400 56.500
[505] 56.600 56.700 56.800 56.900 57.000 57.300 57.400 57.500 57.600 57.700 57.800 58.000
[517] 58.100 58.200 58.400 58.600 58.700 58.800 58.900 59.100 59.500 59.600 59.700 59.800
[529] 59.900 60.000 60.100 60.200 60.300 60.400 60.500 60.700 60.800 60.900 61.000 61.100
[541] 61.200 61.300 61.500 61.600 61.700 61.900 62.000 62.200 62.300 62.400 62.500 62.600
[553] 62.700 62.800 63.000 63.100 63.200 63.300 63.400 63.500 63.600 63.700 63.800 63.900
[565] 64.000 64.100 64.300 64.400 64.500 64.700 64.900 65.000 65.100 65.200 65.400 65.500
[577] 65.700 65.900 66.000 66.100 66.200 66.700 67.000 67.400 67.500 67.600 67.700 67.900
[589] 68.000 68.100 68.300 68.400 68.700 68.800 69.000 69.300 69.800 69.900 70.000 70.100
[601] 70.200 70.300 70.400 70.500 70.700 70.800 71.000 71.100 71.300 71.600 72.000 72.200
[613] 72.600 73.100 73.200 73.400 73.500 73.700 73.800 73.900 74.000 74.200 74.400 74.900
[625] 75.100 75.200 75.500 75.800 76.000 76.300 76.500 76.600 76.700 77.800 77.900 78.200
[637] 78.600 78.800 79.000 79.100 79.200 79.300 79.500 79.600 79.900 80.600 80.700 80.900
[649] 81.100 81.300 81.500 81.800 82.100 82.200 82.900 83.000 83.400 83.500 83.700 83.800
[661] 84.000 84.100 84.400 84.500 84.800 85.100 85.300 85.400 85.500 85.600 85.800 85.900
[673] 86.000 86.400 86.500 86.700 86.800 87.000 87.100 87.200 87.300 87.400 87.500 87.600
[685] 87.700 87.800 87.900 88.000 88.100 88.200 88.600 88.800 88.900 89.300 89.900 90.000
[697] 90.100 90.200 90.300 90.400 90.500 90.800 91.000 91.300 91.400 91.500 91.600 91.900
[709] 92.000 92.100 92.500 92.600 92.700 92.900 93.000 93.200 93.300 93.400 93.500 93.600
[721] 93.700 93.800 94.000 94.200 94.400 94.500 94.600 94.700 94.800 94.900 95.000 95.200
[733] 95.300 95.700 95.800 96.000 96.300 96.900 97.000 97.400 97.500 97.800 98.000 98.200
[745] 98.400 98.700 99.000 99.300 99.400 99.600 99.700 99.800 99.900 100.000 100.100 100.200
[757] 100.600 100.800 101.000 101.200 101.400 102.100 102.300 102.400 102.500 102.600 103.200 103.400
[769] 104.000 104.100 104.300 105.000 105.200 106.300 106.400 106.500 106.700 106.800 107.000 107.200
[781] 107.400 107.500 107.600 107.800 107.900 108.000 108.100 108.300 108.700 108.800 109.000 109.100
[793] 109.200 109.300 109.500 110.000 111.100 111.300 113.200 113.500 113.700 114.500 114.800 115.000
[805] 115.200 115.500 115.700 115.800 116.000 116.400 116.500 116.600 116.800 117.100 117.800 118.000
[817] 118.200 118.900 119.000 119.400 119.500 120.000 121.000 121.400 121.600 122.200 122.500 123.100
[829] 123.200 123.400 123.600 123.800 123.900 124.200 124.700 125.000 125.100 125.400 126.000 126.200
[841] 126.700 127.100 127.300 127.500 127.700 128.900 129.100 129.400 131.200 131.400 132.000 132.300
[853] 132.600 133.700 133.800 134.000 135.000 136.000 136.100 136.200 136.300 136.400 136.800 137.200
[865] 138.000 138.600 138.800 138.900 139.100 139.400 139.600 139.800 140.000 140.200 140.500 140.800
[877] 141.400 141.700 142.000 142.200 143.200 143.600 143.700 143.800 144.000 144.200 144.300 144.500
[889] 145.000 145.400 145.800 146.000 146.100 146.400 146.700 146.800 147.000 147.200 147.900 148.000
[901] 148.300 149.100 150.000 150.100 150.300 150.600 150.800 151.200 151.600 152.500 152.800 152.900
[913] 153.700 154.200 154.300 154.600 155.000 155.700 155.900 156.500 157.100 157.900 158.000 158.200
[925] 158.600 158.700 158.800 159.000 159.400 159.500 159.900 160.000 160.100 160.800 161.000 161.800
[937] 162.100 162.200 162.700 163.300 163.600 164.000 164.500 164.600 164.800 164.900 165.300 165.400
[949] 165.800 166.000 166.200 166.400 166.500 166.800 166.900 167.000 167.300 167.500 167.700 167.800
[961] 167.900 168.000 168.300 168.500 168.800 169.000 169.300 169.500 169.600 169.800 170.000 170.300
[973] 171.200 171.300 171.500 171.700 172.200 172.300 172.400 172.500 173.000 173.200 173.300 173.500
[985] 173.600 173.900 174.000 174.200 174.400 174.600 174.800 174.900 175.000 175.600 175.900 176.000
[997] 176.100 176.500 177.100 177.300
[ reached getOption("max.print") -- omitted 851 entries ]
$分局
[1] "中分局" "北分局" "南分局"
$工務段
[1] "0" "大甲工務段" "中壢工務段" "內湖工務段" "斗南工務段" "木柵工務段" "白河工務段"
[8] "岡山工務段" "南投工務段" "屏東工務段" "苗栗工務段" "新營工務段" "頭城工務段" "關西工務段"
$向
[1] "北側" "西側" "東側" "附近" "南側"
$`車道線(側)名稱`
[1] "入口內側車道" "入口匝道" "入口匝道內側車道" "入口匝道外側車道"
[5] "土城入口匝道單線車道" "大型車專用道內側車道" "中內" "中外"
[9] "中和出口匝道單一車道" "中和出口匝道單線車道" "中線" "中線車道"
[13] "五股入口匝道" "五股出口匝道外側車道" "五楊高架內側車道" "五楊高架外側車道"
[17] "五楊高架外側路肩" "五楊高架高乘載車道" "仁德系統東往南" "內側"
[21] "內側車道" "內側路肩" "內側邊坡" "內壢出口匝道內側車道"
[25] "出口匝道" "出口匝道(內側)" "出口匝道(潭子系統交" "出口匝道中線車道"
[29] "出口匝道內側車道" "出口匝道內側車道(林" "出口匝道外側車道" "出口匝道往林口A出口"
[33] "出口匝道輔助車道" "出口外側車道" "出口交流道" "出口專用車道"
[37] "加速車道" "北斗出口匝道" "北往西匝道" "北往東出口匝道"
[41] "匝道" "匝道入口" "台66入口匝道" "台74線入口匝道"
[45] "台86線匯國一道東往南" "台88接國1匝道內側車道" "外側" "外側車道"
[49] "外側兩車道內側車道" "外側減速車道" "外側路肩" "交岔路口"
[53] "交流道" "服務區" "服務區入口匝道" "林口A出口匝道中線車"
[57] "爬坡" "爬坡車道" "便道" "南向出口匝道"
[61] "南桃園入口匝道(國際" "員林出口匝道" "桃園出口匝道外側車道" "泰山轉接道內側"
[65] "泰山轉接道內側車道" "高架" "高架中線車道" "高架內側"
[69] "高架內側車道" "高架內側車道(泰山轉" "高架出口匝道" "高架外側車道"
[73] "高架外側路肩" "高架泰山轉接道內側" "高架高乘載車道" "高架專用車道"
[77] "高架輔助車道往環河北" "高乘載車道" "麻豆交流道出口匝道" "單一車道"
[81] "減速車道" "開放時段外側路肩" "集散道" "集散道-外側車道"
[85] "新竹系統匝道" "新營交流道入口匝道" "路肩" "輔助"
[89] "輔助中線車道" "輔助內側" "輔助內側車道" "輔助外側"
[93] "輔助外側車道" "輔助外側車道(往新莊" "輔助外側車道(往新莊)" "輔助車道"
[97] "輔助車道(往林口B交流" "輔助車道(往高架)" "輔助車道外側車道" "輔助車道往五股出口"
[101] "輔助車道往五楊高架" "輔助車道往高公局方向" "輔助車道往高架" "輔助往高架"
[105] "樹林入口匝道單線車道" "機場系統出口匝道" "機場系統西往南匝道外" "機場系統東往南入口匝"
[109] "興楠入口匝道" "關西服務區入口匝道" "霧峰出口匝道"
$`24小時內死亡人數`
[1] 0
$`2-30日內死亡人數`
[1] 0 1 2
$受傷
[1] 0 1 2 3 4 5 6 7 8 9 10 11 12 13
$天候
[1] "雨" "風" "陰" "晴" "暴雨" "霧或煙"
$`道路照明設備(11207新增)`
[1] "日間自然光線" "有照明且開啟"
[3] "有照明未開啟或故障" "夜間(或隧道、地下道、涵洞)有照明"
[5] "夜間(或隧道、地下道、涵洞)無照明" "晨或暮光"
[7] "無照明"
$道路類別
[1] "其他" "國道" "專用道路" "縣道"
$速限
[1] 0 20 25 30 40 50 60 70 80 90 100 110
$道路型態
[1] "三岔路" "四岔路" "休息站或服務區" "多岔路" "其他"
[6] "坡路" "直路" "高架道路" "涵洞" "圓環"
[11] "廣場" "隧道" "彎曲路及附近"
$事故位置
[1] "一般車道(未劃分快慢車道)" "加速車道" "交叉口附近"
[4] "交叉路口內" "交通島(含槽化線)" "快車道"
[7] "其他" "直路" "直線匝道"
[10] "減速車道" "路肩、路緣" "慢車道"
[13] "機車專用道" "機車優先道" "環道匝道"
$`路面狀況-路面鋪裝`
[1] "水泥" "快車道" "直線匝道" "柏油"
$`路面狀況-路面狀態`
[1] "冰雪" "油滑" "乾燥" "濕潤"
$`路面狀況-路面缺陷`
[1] "有坑洞" "無缺陷" "路面鬆軟"
$`道路障礙-障礙物`
[1] "其他障礙物" "無障礙物" "路上有停車" "道路工事(程)中"
$`道路障礙-視距`
[1] "良好" "其他" "建築物" "路上停放車輛" "樹木、農作物"
$`號誌-號誌種類`
[1] "行車管制號誌" "行車管制號誌(附設行人專用號誌)" "閃光號誌"
[4] "無號誌"
$`號誌-號誌動作`
[1] "不正常" "正常" "無號誌"
$`車道劃分設施-分向設施`
[1] "附標記" "無分向設施" "無標記" "寬式(50公分以上)" "寬式附柵欄"
[6] "寬式無柵欄"
$`車道劃分設施-分道設施-快車道或一般車道間`
[1] "未繪設車道線" "車道線(附標記)" "車道線(無標記)" "禁止變換車道線(附標記)"
[5] "禁止變換車道線(無標記)"
$`車道劃分設施-分道設施-快慢車道間`
[1] "未繪設快慢車道分隔線" "快慢車道分隔線" "寬式快慢車道分隔島(50公分以上)"
[4] "寬式快慢車道分隔島(無柵欄)"
$`車道劃分設施-分道設施-路面邊線`
[1] "有" "無"
$事故類型及型態代碼
[1] 3 6 7 9 10 11 12 13 14 15 16 17 18 19 20 21 23 24 26 27 28 29 30
$事故類型及型態2
[1] "同向擦撞" "其他" "穿越道路中" "倒車撞"
[5] "追撞" "側撞" "從停車後(或中)穿出" "路上翻車、摔倒"
[9] "路口交岔撞" "對向擦撞" "對撞" "撞工程施工"
[13] "撞交通島" "撞非固定設施" "撞動物" "撞號誌、標誌桿"
[17] "撞路樹、電桿" "撞護欄(樁)" "衝出路外" "衝進路中"
$當事者順位
[1] 1
$`當事者屬(性)別`
[1] "女" "男" "無或物(動物、堆置物)" "肇事逃逸尚未查獲"
$當事者事故發生時年齡
[1] -1 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37
[25] 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61
[49] 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 86
[73] 87 90 103
$受傷程度
[1] "2-30日內死亡" "不明" "未受傷" "受傷"
$保護裝備
[1] "不明" "未戴安全帽或未繫安全帶(未使用幼童安全椅)"
[3] "未戴案全帽或未繫安全帶(未使用幼童安全椅)" "其他(無需使用保護裝備之人)"
[5] "戴半寬式安全帽" "戴安全帽或繫安全帶(使用幼童安全椅)"
[7] "戴非半寬式安全帽" "繫安全帶(使用幼童安全椅)"
$`行動電話、電腦或其他相類功能裝置名稱`
[1] "不明" "未使用" "使用手持或有礙駕駛安全"
[4] "使用免持或未有礙駕駛安全" "非駕駛人"
$`當事者區分(大類別)`
[1] "人" "大客車" "大貨車" "小客車"
[5] "小客車(含客、貨兩用)" "小貨車(含客、貨兩用)" "半聯結車" "全聯結車"
[9] "曳引車" "其他車" "軍車" "特種車"
[13] "慢車" "機車"
$`當事者區分(類別)`
[1] "大型重型1(550C.C.以上)" "大型重型2(250-550C.C.)" "小型車"
[4] "工程車" "公營公車" "公營客運"
[7] "民營公車" "民營客運" "自用"
[10] "行人" "計程車" "乘客"
[13] "租賃小貨車" "租賃車" "動力機械"
[16] "普通重型" "普通輕型" "腳踏自行車"
[19] "遊覽車" "營業用"
$車輛用途
[1] "其他" "非駕駛人及乘客" "砂石車" "校車" "教練車"
[6] "殘障特製車" "裝載危險物品車"
$飲酒情形名稱
[1] "不明"
[2] "呼氣未滿0.15 mg/L或血液檢測未滿0.03%"
[3] "呼氣達0.15以上未滿0.25 mg/L或血液 0.03%以上未滿0.05%"
[4] "呼氣達0.25以上未滿0.40 mg/L或血液 0.05%以上未滿0.08%"
[5] "呼氣達0.4以上未滿0.55mg/L或血液達 0.08%以上未滿0.11%"
[6] "呼氣達0.55以上未滿0.80 mg/L或血液 0.11%以上未滿0.16%"
[7] "呼氣檢測 0.80 mg/L以上或血液檢測 0.16%以上"
[8] "非駕駛人，未檢測"
[9] "無法檢測"
[10] "經呼氣檢測0.16~0.25mg/L或血液檢測0.031%~0.05%"
[11] "經呼氣檢測0.26~0.40mg/L或血液檢測0.051%~0.08%"
[12] "經呼氣檢測0.41~0.55mg/L或血液檢測0.081%~0.11%"
[13] "經呼氣檢測0.56~0.80mg/L或血液檢測0.111%~0.16%"
[14] "經呼氣檢測未超過0.15mg/L或血液檢測未超過0.03%"
[15] "經呼氣檢測超0.80~mg/L或血液檢測超過0.16%"
[16] "經檢測無酒精反應"
[17] "經觀察未飲酒"
[18] "駕駛人不明"
$`初步分析研判子類別-主要`
[1] "不明原因肇事"
[2] "方向不定(不包括危險駕車)"
[3] "方向操縱系統故障"
[4] "打瞌睡或疲勞駕駛(包括連續駕車8小時)"
[5] "未依規定使用燈光"
[6] "未依規定減速"
[7] "未依規定讓車"
[8] "未依標誌或標線穿越道路"
[9] "未注意車前狀態"
[10] "未保持行車安全距離"
[11] "未保持行車安全間隔"
[12] "因光線、視線遮蔽致生事故"
[13] "有號誌路口，轉彎車未讓直行車先行"
[14] "吸食違禁物後駕駛失控"
[15] "吸食違禁物駕駛"
[16] "車輛未依規定暫停讓行人先行"
[17] "車輛或機械操作不當(慎)"
[18] "車輛拋錨未採安全措施"
[19] "車輛附屬機具或車門未盡安全措施"
[20] "車輛零件脫落"
[21] "車輪脫落或輪胎爆裂"
[22] "使用手持行動電話"
[23] "使用車輛自動駕駛或先進駕駛輔助系統設備(裝置)不符規定"
[24] "其他不當駕車行為"
[25] "其他引起事故之故障"
[26] "其他引起事故之疏失或行為"
[27] "其他引起事故之違規或不當行為"
[28] "其他未依規定讓車"
[29] "其他裝載不當肇事"
[30] "其他機件失靈或故障"
[31] "尚未發現肇事因素"
[32] "拋錨未採安全措施"
[33] "物品(件)滾(滑行)或飛(掉)落"
[34] "恍神、緊張、心不在焉分心駕駛"
[35] "相關跡證不足且無具體影像紀錄，當事人各執一詞，經分析後無法釐清肇事原因"
[36] "乘客、車上動(生)物干擾分心駕駛"
[37] "倒車未依規定"
[38] "疲勞(患病)駕駛失控"
[39] "起步未注意其他車(人)安全"
[40] "起步時未注意安全"
[41] "迴轉未依規定"
[42] "逆向行駛"
[43] "酒醉(後)駕駛"
[44] "酒醉(後)駕駛失控"
[45] "閃避不當(慎)"
[46] "停車操作時，未注意其他車(人)安全"
[47] "停車操作時未注意安全"
[48] "動物竄出"
[49] "患病或服用藥物(疲勞)駕駛"
[50] "無號誌路口，支線道未讓幹線道先行"
[51] "無號誌路口，轉彎車未讓直行車先行"
[52] "發生事故後，未採取安全措施"
[53] "超速失控"
[54] "超速駕駛"
[55] "飲食、抽(點)菸、拿(撿)物品分心駕駛"
[56] "裝載未盡安全措施"
[57] "裝載貨物不穩妥"
[58] "路況危險無安全(警告)設施"
[59] "載貨超重而失控"
[60] "違反車輛改道標誌"
[61] "違反其他標誌(線)禁制"
[62] "違反特定標誌(線)禁制"
[63] "違反禁止進入標誌"
[64] "違反禁行車種標誌(字)"
[65] "違反號誌管制或指揮"
[66] "違規停車或暫停不當而肇事"
[67] "違規超車"
[68] "肇事逃逸未查獲，無法查明肇因"
[69] "操作、觀看行車輔助或娛樂性顯示設備"
[70] "變換車道不當"
[71] "變換車道或方向不當"
[72] "觀看其他事故、活動、道路環境或車外資訊分心駕駛"
$`肇事逃逸(是否肇逃)`
[1] "否" "是"summary(A2_112_1_10.unique) Length Class Mode
年 1 -none- numeric
月 12 -none- numeric
日 31 -none- numeric
時 24 -none- character
分 60 -none- character
秒 60 -none- character
事故類別 1 -none- character
縣市 17 -none- character
市區鄉鎮 157 -none- character
路線 10 -none- character
公里 404 -none- numeric
公尺 13 -none- numeric
里程 1851 -none- numeric
分局 3 -none- character
工務段 14 -none- character
向 5 -none- character
車道線(側)名稱 111 -none- character
24小時內死亡人數 1 -none- numeric
2-30日內死亡人數 3 -none- numeric
受傷 14 -none- numeric
天候 6 -none- character
道路照明設備(11207新增) 7 -none- character
道路類別 4 -none- character
速限 12 -none- numeric
道路型態 13 -none- character
事故位置 15 -none- character
路面狀況-路面鋪裝 4 -none- character
路面狀況-路面狀態 4 -none- character
路面狀況-路面缺陷 3 -none- character
道路障礙-障礙物 4 -none- character
道路障礙-視距 5 -none- character
號誌-號誌種類 4 -none- character
號誌-號誌動作 3 -none- character
車道劃分設施-分向設施 6 -none- character
車道劃分設施-分道設施-快車道或一般車道間 5 -none- character
車道劃分設施-分道設施-快慢車道間 4 -none- character
車道劃分設施-分道設施-路面邊線 2 -none- character
事故類型及型態代碼 23 -none- numeric
事故類型及型態2 20 -none- character
當事者順位 1 -none- numeric
當事者屬(性)別 4 -none- character
當事者事故發生時年齡 75 -none- numeric
受傷程度 4 -none- character
保護裝備 8 -none- character
行動電話、電腦或其他相類功能裝置名稱 5 -none- character
當事者區分(大類別) 14 -none- character
當事者區分(類別) 20 -none- character
車輛用途 7 -none- character
飲酒情形名稱 18 -none- character
初步分析研判子類別-主要 72 -none- character
肇事逃逸(是否肇逃) 2 -none- characterprint(paste0("There have ", sum(is.na(A2_112_1_10)), " NA(s) in this dataset."))[1] "There have 432 NA(s) in this dataset."data plot
# all
A2_112_1_10.plot <- A2_112_1_10
name <- names(A2_112_1_10.unique)
for (i in 1:ncol(A2_112_1_10.plot)) {
barplot(table(A2_112_1_10.plot[ ,i]), main = name[i], xlab = "類別", ylab = "頻率", las = 2)
}
| # 112 Jan. to Oct. A3 accidents (A3_112_1_10) # summary |
|---|
| # 113 Jan. to Feb. A1A2A3 accidents (A1A2A3_113_1_2) # summary |
r library(dplyr) summary(A1A2A3_113_1_2) |
| ``` 年 月 日 時 分 Min. :2024 Min. :1.000 Min. : 1.00 Length:18570 Length:18570 1st Qu.:2024 1st Qu.:1.000 1st Qu.: 8.00 Class :character Class :character Median :2024 Median :1.000 Median :14.00 Mode :character Mode :character Mean :2024 Mean :1.471 Mean :15.13 3rd Qu.:2024 3rd Qu.:2.000 3rd Qu.:22.00 Max. :2024 Max. :2.000 Max. :31.00 |
| 秒 事故類別 縣市 市區鄉鎮 路線 Length:18570 Length:18570 Length:18570 Length:18570 Length:18570 Class :character Class :character Class :character Class :character Class :character Mode :character Mode :character Mode :character Mode :character Mode :character |
| 公里 公尺 里程 分局 工務段 Min. : 0.0 Min. : 0.00 Min. : 0.0 Length:18570 Length:18570 1st Qu.: 37.0 1st Qu.: 0.25 1st Qu.: 37.1 Class :character Class :character Median : 96.0 Median :400.00 Median : 96.7 Mode :character Mode :character Mean :145.7 Mean :377.17 Mean :146.1 3rd Qu.:237.8 3rd Qu.:600.00 3rd Qu.:237.9 Max. :430.0 Max. :900.00 Max. :430.3 |
| 向 車道線(側)名稱 24小時內死亡人數 2-30日內死亡人數 受傷 Length:18570 Length:18570 Min. :0.000000 Min. :0.000000 Min. : 0.000 Class :character Class :character 1st Qu.:0.000000 1st Qu.:0.000000 1st Qu.: 0.000 Mode :character Mode :character Median :0.000000 Median :0.000000 Median : 0.000 Mean :0.004416 Mean :0.001615 Mean : 0.238 3rd Qu.:0.000000 3rd Qu.:0.000000 3rd Qu.: 0.000 Max. :1.000000 Max. :1.000000 Max. :11.000 |
| 天候 道路照明設備(11207新增) 道路類別 速限 道路型態 Length:18570 Length:18570 Length:18570 Min. : 0.0 Length:18570 Class :character Class :character Class :character 1st Qu.: 90.0 Class :character Mode :character Mode :character Mode :character Median :100.0 Mode :character Mean : 88.8 3rd Qu.:110.0 Max. :110.0 |
| 事故位置 路面狀況-路面鋪裝 路面狀況-路面狀態 路面狀況-路面缺陷 道路障礙-障礙物 Length:18570 Length:18570 Length:18570 Length:18570 Length:18570 Class :character Class :character Class :character Class :character Class :character Mode :character Mode :character Mode :character Mode :character Mode :character |
| 道路障礙-視距 號誌-號誌種類 號誌-號誌動作 車道劃分設施-分向設施 Length:18570 Length:18570 Length:18570 Length:18570 Class :character Class :character Class :character Class :character Mode :character Mode :character Mode :character Mode :character |
| 車道劃分設施-分道設施-快車道或一般車道間 車道劃分設施-分道設施-快慢車道間 Length:18570 Length:18570 Class :character Class :character Mode :character Mode :character |
| 車道劃分設施-分道設施-路面邊線 事故類型及型態代碼 事故類型及型態2 當事者順位 Length:18570 Min. : 0.00 Length:18570 Min. : 0.000 Class :character 1st Qu.:13.00 Class :character 1st Qu.: 1.000 Mode :character Median :13.00 Mode :character Median : 2.000 Mean :14.52 Mean : 1.881 3rd Qu.:13.00 3rd Qu.: 2.000 Max. :30.00 Max. :31.000 NA’s :13 當事者屬(性)別 當事者事故發生時年齡 受傷程度 保護裝備 Length:18570 Min. :-1.00 Length:18570 Length:18570 Class :character 1st Qu.:29.00 Class :character Class :character Mode :character Median :39.00 Mode :character Mode :character Mean :38.39 3rd Qu.:51.00 Max. :90.00 |
| 行動電話、電腦或其他相類功能裝置名稱 當事者區分(大類別) 當事者區分(類別) 車輛用途 Length:18570 Length:18570 Length:18570 Length:18570 Class :character Class :character Class :character Class :character Mode :character Mode :character Mode :character Mode :character |
| 飲酒情形名稱 初步分析研判子類別-主要 肇事逃逸(是否肇逃) Length:18570 Length:18570 Length:18570 Class :character Class :character Class :character Mode :character Mode :character Mode :character |
| ``` |
r n <- ncol(A1A2A3_113_1_2) A1A2A3_113_1_2.unique <- sapply(1:n, function(x){A1A2A3_113_1_2[ ,x] %>% unique() %>% unlist() %>% unname() %>% sort() %>% return()}) names(A1A2A3_113_1_2.unique) = colnames(A1A2A3_113_1_2) print(A1A2A3_113_1_2.unique) |
| ``` $年 [1] 2024 |
| $月 [1] 1 2 |
| $日 [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 |
| $時 [1] “00” “01” “02” “03” “04” “05” “06” “07” “08” “09” “10” “11” “12” “13” “14” “15” “16” “17” “18” [20] “19” “20” “21” “22” “23” |
| $分 [1] “00” “01” “02” “03” “04” “05” “06” “07” “08” “09” “10” “11” “12” “13” “14” “15” “16” “17” “18” [20] “19” “20” “21” “22” “23” “24” “25” “26” “27” “28” “29” “30” “31” “32” “33” “34” “35” “36” “37” [39] “38” “39” “40” “41” “42” “43” “44” “45” “46” “47” “48” “49” “50” “51” “52” “53” “54” “55” “56” [58] “57” “58” “59” |
| $秒 [1] “00” “01” “02” “03” “04” “05” “06” “07” “08” “09” “10” “11” “12” “13” “14” “15” “16” “17” “18” [20] “19” “20” “21” “22” “23” “24” “25” “26” “27” “28” “29” “30” “31” “32” “33” “34” “35” “36” “37” [39] “38” “39” “40” “41” “42” “43” “44” “45” “46” “47” “48” “49” “50” “51” “52” “53” “54” “55” “56” [58] “57” “58” “59” |
| $事故類別 [1] “A1” “A2” “A3” |
| $縣市 [1] “宜蘭縣” “南投縣” “屏東縣” “苗栗縣” “桃園市” “高雄市” “基隆市” “雲林縣” “新北市” “新竹市” “新竹縣” [12] “嘉義市” “嘉義縣” “彰化縣” “臺中市” “臺北市” “臺南市” |
| $市區鄉鎮 [1] “七堵區” “九如鄉” “八德區” “三民區” “三重區” “三峽區” “三義鄉” “下營區” “土城區” “大甲區” [11] “大同區” “大安區” “大村鄉” “大肚區” “大林鎮” “大社區” “大埤鄉” “大雅區” “大園區” “大溪區” [21] “大寮區” “大樹區” “小港區” “中山區” “中和區” “中埔鄉” “中寮鄉” “中壢區” “五股區” “五結鄉” [31] “仁武區” “仁德區” “內湖區” “公館鄉” “六甲區” “太保市” “文山區” “斗六市” “斗南鎮” “水上鄉” [41] “冬山鄉” “北區” “古坑鄉” “外埔區” “左營區” “平鎮區” “民雄鄉” “永康區” “永靖鄉” “田尾鄉” [51] “田寮區” “白河區” “石碇區” “名間鄉” “后里區” “安定區” “安南區” “安樂區” “汐止區” “竹山鎮” [61] “竹北市” “竹田鄉” “竹東鎮” “竹南鎮” “竹崎鄉” “西屯區” “西區” “西港區” “西湖鄉” “西螺鎮” [71] “壯圍鄉” “沙鹿區” “秀水鄉” “芎林鄉” “里港鄉” “和美鎮” “坪林區” “官田區” “宜蘭市” “岡山區” [81] “東山區” “東區” “松山區” “林口區” “林內鄉” “林邊鄉” “芬園鄉” “花壇鄉” “虎尾鎮” “長治鄉” [91] “阿蓮區” “前金區” “前鎮區” “南屯區” “南州鄉” “南投市” “南港區” “後壁區” “後龍鎮” “柳營區” [101] “苑裡鎮” “苓雅區” “苗栗市” “香山區” “埔心鄉” “埔里鎮” “埔鹽鄉” “崁頂鄉” “桃園區” “泰山區” [111] “烏日區” “神岡區” “草屯鎮” “高樹鄉” “國姓鄉” “埤頭鄉” “梓官區” “清水區” “通霄鎮” “造橋鄉” [121] “鳥松區” “鹿草鄉” “麻豆區” “善化區” “湖口鄉” “新化區” “新市區” “新店區” “新營區” “新豐鄉” [131] “楊梅區” “楠梓區” “溪口鄉” “溪州鄉” “溪湖鎮” “路竹區” “彰化市” “旗山區” “銅鑼鄉” “鳳山區” [141] “潭子區” “潮州鎮” “樹林區” “橋頭區” “燕巢區” “頭份市” “頭城鎮” “頭屋鄉” “龍井區” “龍崎區” [151] “龍潭區” “龜山區” “礁溪鄉” “豐原區” “羅東鎮” “關西鎮” “關廟區” “霧峰區” “寶山鄉” “蘆竹區” [161] “蘇澳鎮” “鶯歌區” “麟洛鄉” “鹽水區” “鹽埔鄉” |
| $路線 [1] “南港連絡道線” “國道10號” “國道1號” “國道2號” “國道3甲” “國道3號” [7] “國道4號” “國道5號” “國道6號” “國道8號” |
| $公里 [1] 0.0 1.0 2.0 3.0 3.6 4.0 5.0 6.0 7.0 8.0 9.0 10.0 11.0 12.0 13.0 14.0 [17] 15.0 16.0 17.0 18.0 19.0 20.0 21.0 22.0 23.0 24.0 25.0 26.0 27.0 28.0 29.0 30.0 [33] 31.0 32.0 33.0 34.0 34.2 35.0 35.7 36.0 37.0 38.0 39.0 40.0 41.0 42.0 43.0 44.0 [49] 45.0 46.0 47.0 48.0 49.0 50.0 50.3 50.8 51.0 52.0 52.4 53.0 54.0 55.0 56.0 57.0 [65] 58.0 59.0 60.0 61.0 62.0 63.0 64.0 65.0 66.0 67.0 68.0 69.0 70.0 71.0 72.0 73.0 [81] 74.0 75.0 76.0 77.0 78.0 79.0 80.0 81.0 82.0 83.0 84.0 85.0 86.0 87.0 88.0 89.0 [97] 90.0 91.0 92.0 93.0 94.0 95.0 96.0 97.0 98.0 99.0 100.0 101.0 102.0 103.0 104.0 105.0 [113] 106.0 107.0 108.0 109.0 110.0 111.0 112.0 113.0 114.0 115.0 116.0 117.0 118.0 119.0 120.0 121.0 [129] 122.0 123.0 124.0 125.0 126.0 127.0 128.0 129.0 130.0 131.0 132.0 133.0 134.0 135.0 136.0 137.0 [145] 138.0 139.0 140.0 141.0 142.0 143.0 144.0 145.0 146.0 147.0 148.0 149.0 150.0 151.0 152.0 153.0 [161] 154.0 155.0 156.0 157.0 158.0 159.0 160.0 161.0 162.0 163.0 164.0 165.0 166.0 167.0 168.0 169.0 [177] 170.0 171.0 172.0 173.0 174.0 175.0 176.0 177.0 178.0 179.0 180.0 181.0 182.0 183.0 184.0 185.0 [193] 186.0 187.0 188.0 189.0 190.0 191.0 192.0 193.0 194.0 195.0 196.0 197.0 198.0 199.0 200.0 201.0 [209] 202.0 203.0 204.0 205.0 206.0 207.0 208.0 209.0 210.0 211.0 212.0 213.0 214.0 215.0 216.0 217.0 [225] 218.0 219.0 220.0 221.0 222.0 223.0 224.0 225.0 226.0 227.0 228.0 229.0 230.0 231.0 232.0 233.0 [241] 234.0 235.0 236.0 237.0 238.0 239.0 240.0 241.0 242.0 243.0 244.0 245.0 246.0 247.0 248.0 249.0 [257] 250.0 251.0 252.0 253.0 254.0 255.0 256.0 257.0 258.0 259.0 260.0 261.0 262.0 263.0 264.0 265.0 [273] 266.0 267.0 268.0 269.0 270.0 271.0 272.0 273.0 274.0 275.0 276.0 277.0 278.0 279.0 280.0 281.0 [289] 282.0 283.0 284.0 285.0 286.0 287.0 288.0 289.0 290.0 291.0 292.0 293.0 294.0 295.0 296.0 297.0 [305] 298.0 299.0 300.0 301.0 302.0 303.0 304.0 305.0 306.0 307.0 308.0 309.0 310.0 311.0 312.0 313.0 [321] 314.0 315.0 316.0 317.0 318.0 319.0 320.0 321.0 322.0 323.0 324.0 325.0 326.0 327.0 328.0 329.0 [337] 330.0 331.0 332.0 333.0 334.0 335.0 336.0 337.0 338.0 339.0 340.0 341.0 342.0 343.0 344.0 345.0 [353] 346.0 347.0 348.0 349.0 350.0 351.0 352.0 353.0 354.0 355.0 356.0 357.0 358.0 359.0 360.0 361.0 [369] 362.0 363.0 364.0 365.0 366.0 367.0 368.0 369.0 370.0 371.0 372.0 373.0 374.0 375.0 376.0 377.0 [385] 378.0 379.0 380.0 381.0 382.0 383.0 384.0 385.0 388.0 389.0 390.0 391.0 395.0 396.0 400.0 405.0 [401] 406.0 407.0 408.0 410.0 411.0 414.0 415.0 416.0 417.0 419.0 420.0 421.0 422.0 423.0 424.0 426.0 [417] 429.0 430.0 |
| $公尺 [1] 0 1 2 4 40 80 100 200 300 400 470 480 500 600 650 700 800 870 900 |
| $里程 [1] 0.000 0.100 0.200 0.300 0.400 0.500 0.600 0.700 0.800 0.900 1.000 1.100 [13] 1.200 1.300 1.400 1.500 1.600 1.700 1.800 1.900 2.000 2.100 2.200 2.300 [25] 2.400 2.500 2.600 2.700 2.800 2.900 3.000 3.100 3.200 3.300 3.400 3.500 [37] 3.600 3.700 3.800 3.900 4.000 4.100 4.200 4.300 4.400 4.500 4.600 4.700 [49] 4.800 4.900 5.000 5.100 5.200 5.300 5.400 5.500 5.600 5.700 5.800 5.900 [61] 6.000 6.001 6.100 6.200 6.400 6.500 6.600 6.700 6.800 6.900 7.000 7.100 [73] 7.200 7.300 7.400 7.500 7.600 7.700 7.900 8.000 8.100 8.200 8.300 8.400 [85] 8.500 8.600 8.700 8.800 8.900 9.000 9.100 9.200 9.300 9.500 9.600 9.700 [97] 9.800 10.000 10.100 10.200 10.300 10.400 10.500 10.600 10.700 10.800 10.900 11.000 [109] 11.100 11.200 11.300 11.400 11.500 11.600 11.700 11.800 11.900 12.000 12.100 12.200 [121] 12.300 12.400 12.500 12.600 12.700 12.800 12.900 13.000 13.100 13.200 13.300 13.400 [133] 13.500 13.600 13.700 13.800 13.900 14.000 14.100 14.200 14.300 14.400 14.500 14.600 [145] 14.700 14.800 14.900 15.000 15.100 15.200 15.300 15.400 15.500 15.600 15.700 15.800 [157] 15.900 16.000 16.100 16.200 16.300 16.400 16.500 16.600 16.700 16.800 16.900 17.000 [169] 17.100 17.200 17.300 17.400 17.500 17.600 17.700 17.800 17.900 18.000 18.100 18.200 [181] 18.300 18.400 18.500 18.600 18.700 18.800 18.900 19.000 19.100 19.200 19.300 19.400 [193] 19.500 19.600 19.700 19.800 19.900 20.000 20.100 20.200 20.300 20.400 20.500 20.600 [205] 20.700 20.800 21.000 21.100 21.200 21.300 21.400 21.500 21.600 21.700 21.800 21.900 [217] 22.000 22.100 22.200 22.300 22.400 22.500 22.600 22.700 22.800 22.900 23.000 23.100 [229] 23.200 23.300 23.400 23.500 23.600 23.700 23.800 23.900 24.000 24.100 24.200 24.300 [241] 24.400 24.600 24.700 24.800 24.900 25.000 25.100 25.200 25.300 25.400 25.500 25.600 [253] 25.700 25.800 25.900 26.000 26.100 26.200 26.400 26.500 26.600 26.700 26.800 26.870 [265] 26.900 27.000 27.100 27.200 27.300 27.400 27.500 27.600 27.700 27.800 27.900 28.000 [277] 28.100 28.200 28.400 28.480 28.500 28.600 28.700 28.800 29.000 29.100 29.200 29.300 [289] 29.400 29.500 29.600 29.700 29.800 29.900 30.000 30.100 30.200 30.300 30.400 30.500 [301] 30.600 30.700 30.800 30.900 31.000 31.100 31.200 31.300 31.400 31.500 31.600 31.700 [313] 31.800 31.900 32.000 32.100 32.200 32.300 32.400 32.500 32.600 32.700 32.800 32.900 [325] 33.000 33.100 33.200 33.300 33.400 33.500 33.600 33.700 33.800 33.900 34.000 34.100 [337] 34.200 34.300 34.400 34.500 34.600 34.700 34.800 34.900 35.000 35.100 35.200 35.300 [349] 35.400 35.500 35.600 35.700 35.800 35.900 36.000 36.100 36.200 36.300 36.400 36.500 [361] 36.600 36.700 36.800 36.900 37.000 37.100 37.200 37.300 37.400 37.500 37.600 37.700 [373] 37.800 37.900 38.000 38.100 38.200 38.400 38.500 38.600 38.700 38.800 38.900 39.000 [385] 39.100 39.200 39.300 39.400 39.500 39.600 39.700 39.800 39.900 40.000 40.100 40.200 [397] 40.300 40.400 40.500 40.600 40.700 40.800 40.900 41.000 41.100 41.200 41.300 41.500 [409] 41.600 41.700 41.800 41.900 42.000 42.100 42.200 42.300 42.400 42.500 42.600 42.700 [421] 42.800 42.900 43.000 43.100 43.200 43.300 43.400 43.500 43.600 43.700 43.800 43.900 [433] 44.000 44.100 44.200 44.300 44.400 44.500 44.600 44.700 44.800 45.000 45.100 45.300 [445] 45.400 45.500 45.600 45.700 45.800 45.900 46.000 46.100 46.200 46.300 46.400 46.500 [457] 46.600 46.700 46.800 46.900 47.000 47.100 47.200 47.300 47.400 47.500 47.600 47.700 [469] 47.800 47.900 48.000 48.400 48.500 48.600 48.700 48.800 48.900 49.000 49.100 49.200 [481] 49.400 49.500 49.600 49.700 49.800 49.900 50.000 50.100 50.200 50.300 50.400 50.500 [493] 50.600 50.700 50.800 50.900 51.000 51.100 51.200 51.300 51.400 51.500 51.600 51.700 [505] 51.800 51.900 52.000 52.100 52.200 52.300 52.400 52.500 52.600 52.700 52.800 52.900 [517] 53.000 53.100 53.200 53.300 53.400 53.600 53.700 53.800 53.900 54.000 54.100 54.300 [529] 54.400 54.500 54.600 54.800 54.900 55.000 55.100 55.200 55.300 55.400 55.500 55.600 [541] 55.700 55.800 55.900 56.000 56.100 56.200 56.300 56.400 56.500 56.600 56.700 56.800 [553] 56.900 57.000 57.100 57.200 57.300 57.400 57.500 57.600 57.700 57.800 57.900 58.000 [565] 58.100 58.200 58.300 58.400 58.500 58.600 58.700 58.800 58.900 59.000 59.100 59.200 [577] 59.300 59.400 59.500 59.600 59.700 59.800 59.900 60.000 60.100 60.200 60.300 60.400 [589] 60.500 60.700 60.800 60.900 61.000 61.100 61.200 61.300 61.400 61.500 61.700 61.800 [601] 61.900 62.000 62.100 62.200 62.300 62.400 62.500 62.600 62.700 62.800 62.900 63.000 [613] 63.100 63.200 63.300 63.400 63.500 63.600 63.700 63.800 63.900 64.000 64.040 64.100 [625] 64.200 64.300 64.400 64.500 64.600 64.700 64.800 65.000 65.300 65.400 65.500 65.600 [637] 65.700 65.800 65.900 66.000 66.100 66.200 66.300 66.500 66.600 66.700 66.800 66.900 [649] 67.000 67.100 67.200 67.300 67.400 67.500 67.700 67.800 67.900 68.000 68.100 68.200 [661] 68.300 68.400 68.500 68.600 68.700 68.800 69.000 69.100 69.200 69.300 69.400 69.500 [673] 69.600 69.800 70.000 70.200 70.300 70.400 70.500 70.600 70.700 70.800 70.900 71.000 [685] 71.100 71.200 71.300 71.400 71.500 71.600 71.700 71.900 72.000 72.100 72.200 72.300 [697] 72.700 73.000 73.300 73.400 73.600 74.000 74.100 74.200 74.400 74.500 74.600 74.800 [709] 74.900 75.000 75.200 75.700 75.900 76.000 76.100 76.200 76.300 76.400 76.500 76.600 [721] 76.700 76.800 77.000 77.100 77.200 77.300 77.500 77.600 77.700 77.900 78.000 78.100 [733] 78.400 78.500 79.000 79.100 79.400 79.600 79.700 80.000 80.100 80.200 80.400 80.600 [745] 80.700 81.000 81.100 81.300 81.500 81.600 81.800 81.900 82.000 82.200 82.300 82.400 [757] 82.500 82.600 82.800 83.000 83.100 83.200 83.600 83.700 83.800 83.900 84.000 84.100 [769] 84.200 84.400 84.500 84.600 84.700 84.800 84.900 85.000 85.100 85.200 85.400 85.500 [781] 85.700 85.800 85.900 86.000 86.100 86.200 86.300 86.400 86.500 86.600 86.700 86.800 [793] 87.000 87.100 87.200 87.300 87.400 87.500 87.600 87.700 87.800 87.900 88.000 88.100 [805] 88.200 88.300 88.400 88.500 88.600 88.700 89.000 89.100 89.200 89.300 89.400 89.600 [817] 89.700 89.800 89.900 90.000 90.200 90.400 90.500 90.600 90.700 90.900 91.000 91.100 [829] 91.200 91.300 91.500 91.600 92.000 92.100 92.200 92.300 92.400 92.500 92.600 92.900 [841] 93.000 93.100 93.200 93.300 93.400 93.500 93.600 93.700 94.000 94.200 94.300 94.400 [853] 94.500 94.600 94.700 94.800 95.000 95.100 95.200 95.300 95.400 95.500 95.600 96.000 [865] 96.100 96.300 96.500 96.600 96.700 96.800 96.900 97.000 97.200 97.300 97.400 97.500 [877] 97.600 97.700 97.800 98.000 98.100 98.200 98.300 98.500 98.600 98.700 98.800 99.000 [889] 99.300 99.400 99.500 99.600 99.700 99.800 99.900 100.000 100.100 100.200 100.500 100.700 [901] 100.800 101.000 101.300 101.400 101.500 102.000 102.100 102.200 102.300 102.400 102.500 102.600 [913] 102.700 102.800 102.900 103.000 103.200 103.700 104.000 104.200 104.300 104.600 104.700 104.800 [925] 104.900 105.000 105.400 105.600 105.800 105.900 106.000 106.200 106.500 106.900 107.000 107.500 [937] 107.700 107.900 108.000 108.100 108.200 108.300 108.400 108.500 108.600 108.700 108.800 108.900 [949] 109.000 109.100 109.200 109.500 109.600 109.700 110.000 110.200 110.300 110.500 110.600 110.700 [961] 111.000 111.200 111.300 111.400 111.500 111.600 111.700 111.900 112.200 112.500 112.700 112.800 [973] 113.600 113.700 114.000 114.200 114.300 114.500 114.600 114.700 114.800 114.900 115.000 115.100 [985] 115.200 115.400 115.800 116.000 116.300 116.500 116.700 116.900 117.000 117.400 117.500 117.800 [997] 117.900 118.000 118.200 118.400 [ reached getOption(“max.print”) – omitted 1547 entries ] |
| $分局 [1] “中分局” “北分局” “南分局” |
| $工務段 [1] “0” “大甲工務段” “中壢工務段” “內湖工務段” “斗南工務段” “木柵工務段” “白河工務段” [8] “岡山工務段” “南投工務段” “屏東工務段” “苗栗工務段” “新營工務段” “頭城工務段” “關西工務段” |
| $向 [1] “口” “北側” “西側” “東側” “附近” “南側” |
$車道線(側)名稱 [1] “F15停車格前”
“九如入口匝道” “九如南向出口匝道” “入口匝道” [5] “入口匝道內側”
“入口匝道內側車道” “入口匝道外側車道” “入口環道” [9]
“三重出口匝道內側車道” “下營系統西往東匝道” “土城出口匝道中線車道”
“大竹入口匝道” [13] “大竹入口匝道(青埔方” “大車專用車道” “中內”
“中內車道” [17] “中外” “中正入口匝道” “中線” “中線車道” [21]
“中線車道(桃園小直線)” “中線車道(高架)” “中壢入口匝道” “中壢服務區” [25]
“中壢服務區(內側車道)” “中壢服務區C區停車區” “中壢服務區E區停車場”
“中壢服務區內側車道” [29] “中壢服務區出口匝道” “中壢服務區外圍道路內”
“中壢服務區停車場C區” “五堵入口匝道” [33] “五楊高架-內側車道”
“五楊高架內側車道” “五楊高架外側車道” “仁武交流道出口匝道” [37]
“仁德B出口匝道” “仁德入口匝道” “仁德系統入口匝道” “仁德系統東往北匝道”
[41] “內側” “內側入口匝道” “內側車道” “內側車道(分流車道)” [45]
“內側車道(高架)” “內側路肩” “內壢出口匝道” “內壢出口匝道(大園方” [49]
“內壢出口匝道外側車道” “王田入口匝道” “出口匝道” “出口匝道-外側車道”
[53] “出口匝道” “出口匝道(五股交流道” “出口匝道(往台64方向)”
“出口匝道(往青埔方向)” [57] “出口匝道(往新屋方向)” “出口匝道(南往東)”
“出口匝道中線車道” “出口匝道內側車道” [61] “出口匝道外側車道”
“出口匝道外側車道(中” “出口匝道往五股” “出口匝道往蓬萊路” [65]
“出口外側” “出口專用車道” “出口專用道” “加油站” [69] “加速車道”
“加速車道(內側)” “北斗入口匝道” “北向豐原入口匝道” [73] “北往西匝道”
“匝道” “匝道內側” “匝道出口” [77] “台74線接國3入口匝道” “台88匯國一匝道”
“台中系統匝道” “右轉專用道” [81] “外側” “外側分流到外側車道”
“外側分流道外側車道” “外側車道” [85] “外側車道(外側分流道)”
“外側車道(林口小直線)” “外側車道(高架)” “外側開放路肩” [89] “外側路肩”
“外側輔助車道” “外側護欄” “左側出口匝道(民雄交” [93] “平鎮系統匝道”
“甲線內側車道” “甲線出口匝道內側車道” “交流道” [97] “休息站”
“地磅站車道” “圳頭出口匝道外側車道” “汐止系統出口匝道” [101]
“竹山入口匝道” “竹山出口匝道” “竹田系統” “西螺服務區” [105]
“系統匝道(西往南)” “岡山入口匝道” “往官田系統出口匝道” “服務區” [109]
“東往南匝道(高架方向)” “林口A入口匝道” “林口A出口匝道內側車”
“林口A出口匝道外側” [113] “林口A出口匝道外側車” “林口B入口匝道”
“林口B出口匝道” “林口B出口匝道中線車” [117] “林口B出口匝道內側車”
“林口B出口匝道外側車” “林口集散道” “爬坡” [121] “爬坡道” “直線匝道”
“虎尾交流道出口匝道” “便道” [125] “南屯出口匝道外側車道” “南往東匝道”
“桃園A出口匝道中線車” “桃園A出口匝道外側車” [129] “桃園B出口匝道-外側車”
“桃園入口匝道內側車道” “泰山轉接道” “泰山轉接道內側車道” [133]
“泰山轉機道內側車道” “泰安服務區” “草屯出口匝道” “高架HOV車道” [137]
“高架中線車道” “高架內側內側” “高架內側車道” “高架出口匝道外側車道”
[141] “高架北向內側車道” “高架北往西系統匝道” “高架外側” “高架外側車道”
[145] “高架泰山轉接道內側車” “高架高乘載車道” “高架專用道專用道”
“高架輔助車道” [149] “高架轉接道內側” “高乘載車道” “高乘載車道(高架)”
“國二甲線外側車道” [153] “國八往國三加速車道” “減速車道”
“開放時段外側路肩” “圓山入口匝道內側車道” [157] “新化休息站小型車停車”
“新台五出口匝道” “新營出口匝道” “楊梅出口匝道內側車道” [161]
“楠梓北向出口匝道” “楠梓南入匝道” “路口” “路竹西往北匝道” [165] “路肩”
“路肩開放之外側路肩” “彰化入口匝道” “彰化交流道出口外側車” [169]
“彰化系統出口” “輔助” “輔助內側” “輔助內側車道” [173]
“輔助內側車道往五股方” “輔助外側” “輔助外側車道” “輔助外側車道往五股方”
[177] “輔助車道” “輔助車道外側車道” “輔助車道往五股”
“輔助車道往五楊高架” [181] “輔助車道往林口B” “輔助車道往桃園A”
“輔助車道往高架” “輔助車道往高架方向” [185] “輔助往高架”
“機場系統入口匝道內側” “機場系統入口匝道外側” “機場系統出口匝道” [189]
“機場系統西往北匝道” “機場系統南往東匝道” “燕巢系統出口匝道”
“環河北路出口匝道外側” [193] “豐原交流道出口匝道” “豐勢出口匝道外側車道”
“邊坡” “關廟服務區小型車D區” [197] “霧峰出口匝道” “寶山匝道入口” |
$24小時內死亡人數 [1] 0 1 |
$2-30日內死亡人數 [1] 0 1 |
| $受傷 [1] 0 1 2 3 4 5 6 7 9 11 |
| $天候 [1] “0” “雨” “風” “風沙” “陰” “晴” “霧或煙” |
$道路照明設備(11207新增) [1] “0”
“有照明且開啟” “有照明未開啟或故障” “無照明” |
| $道路類別 [1] “0” “市區道路” “快速(公)道” “村里道路” “其他” “國道” “專用道路” |
| $速限 [1] 0 10 20 25 30 40 50 60 70 80 90 100 101 110 |
| $道路型態 [1] “0” “三岔路” “四岔路” “休息站或服務區” “地下道” [6] “多岔路” “其他” “坡路” “直路” “高架道路” [11] “無遮斷器” “圓環” “廣場” “橋樑” “隧道” [16] “彎曲路及附近” |
| $事故位置 [1] “0” “一般車道(未劃分快慢車道)” “加速車道” [4] “交叉口附近” “交叉路口內” “交通島(含槽化線)” [7] “自行車專用道” “行人穿越道” “快車道” [10] “其他” “直路” “直線匝道” [13] “迴轉道” “高架道路” “減速車道” [16] “路肩、路緣” “慢車道” “隧道” [19] “環道匝道” |
$路面狀況-路面鋪裝 [1] “0” “水泥” “快車道”
“其他鋪裝” “柏油” “無鋪裝” |
$路面狀況-路面狀態 [1] “0” “冰雪” “乾燥”
“濕潤” |
$路面狀況-路面缺陷 [1] “0”
“突出(高低)不平” “無缺陷” “路面鬆軟” |
$道路障礙-障礙物 [1] “0” “有堆積物”
“其他障礙物” “無障礙物” “路上有停車” [6] “道路工事(程)中” |
$道路障礙-視距 [1] “0” “良好” “建築物”
“路上停放車輛” “樹木、農作物” “彎道” |
$號誌-號誌種類 [1] “0” “行車管制號誌”
“行車管制號誌(附設行人專用號誌)” [4] “閃光號誌” “無號誌” |
$號誌-號誌動作 [1] “0” “正常” “無動作”
“無號誌” |
$車道劃分設施-分向設施 [1] “0” “附標記”
“窄式附柵欄” “窄式無柵欄” “無分向設施” [6] “無標記”
“寬式(50公分以上)” |
$車道劃分設施-分道設施-快車道或一般車道間
[1] “0” “未繪設車道線” “車道線(附標記)” “車道線(無標記)” [5]
“禁止變換車道線(附標記)” “禁止變換車道線(無標記)” |
$車道劃分設施-分道設施-快慢車道間 [1] “0”
“未繪設快慢車道分隔線” “快慢車道分隔線” [4] “窄式快慢車道分隔島(附柵欄)”
“窄式快慢車道分隔島(無柵欄)” “寬式快慢車道分隔島(50公分以上)” |
$車道劃分設施-分道設施-路面邊線 [1] “0”
“有” “無” |
| $事故類型及型態代碼 [1] 0 3 5 6 7 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 |
| $事故類型及型態2 [1] “0” “同向擦撞” “在路上作業中” “其他” [5] “穿越道路中” “倒車撞” “追撞” “側撞” [9] “從停車後(或中)穿出” “路上翻車、摔倒” “路口交岔撞” “對向擦撞” [13] “對撞” “撞工程施工” “撞交通島” “撞非固定設施” [17] “撞建築物” “撞動物” “撞號誌、標誌桿” “撞路樹” [21] “撞電桿” “撞橋樑(橋墩)” “撞護欄(樁)” “衝出路外” [25] “衝進路中” |
| $當事者順位 [1] 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 |
$當事者屬(性)別 [1] “0” “女” “男”
“無或物(動物、堆置物)” [5] “肇事逃逸尚未查獲” |
| $當事者事故發生時年齡 [1] -1 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 [34] 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 [67] 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 |
| $受傷程度 [1] “0” “2-30日內死亡” “24小時內死亡” “不明” “未受傷” “受傷” |
| $保護裝備 [1] “0” “不明” [3] “未戴安全帽或未繫安全帶(未使用幼童安全椅)” “其他(無需使用保護裝備之人)” [5] “戴半罩式安全帽” “戴非半罩式安全帽” [7] “繫安全帶(使用幼童安全椅)” |
$行動電話、電腦或其他相類功能裝置名稱 [1]
“0” “不明” “未使用” [4] “使用手持或有礙駕駛安全”
“使用免持或未有礙駕駛安全” “非駕駛人” |
$當事者區分(大類別) [1] “0” “人” “大客車”
“大貨車” [5] “小客車(含客、貨兩用)” “小貨車(含客、貨兩用)” “半聯結車”
“全聯結車” [9] “曳引車” “其他車” “軍車” “特種車” [13] “慢車” “機車” |
$當事者區分(類別) [1] “0”
“大型重型2(250-550C.C.)” “小型車” [4] “公營公車” “公營客運” “民營公車”
[7] “民營客運” “自用” “自用大客車” [10] “行人” “其他人” “其他車” [13]
“其他特種車” “計程車” “乘客” [16] “租賃小貨車” “租賃車” “動力機械” [19]
“救護車” “普通重型” “微型電動二輪車” [22] “載重車” “遊覽車” “營業用”
[25] “警備車” |
| $車輛用途 [1] “0” “幼童車” “其他” “非駕駛人及乘客” “砂石車” [6] “裝載危險物品車” |
| $飲酒情形名稱 [1] “0” [2] “呼氣未滿0.15 mg/L或血液檢測未滿0.03%” [3] “呼氣達0.15以上未滿0.25 mg/L或血液 0.03%以上未滿0.05%” [4] “呼氣達0.25以上未滿0.40 mg/L或血液 0.05%以上未滿0.08%” [5] “呼氣達0.4以上未滿0.55mg/L或血液達 0.08%以上未滿0.11%” [6] “呼氣達0.55以上未滿0.80 mg/L或血液 0.11%以上未滿0.16%” [7] “呼氣檢測 0.80 mg/L以上或血液檢測 0.16%以上” [8] “非駕駛人，未檢測” [9] “無法檢測” [10] “經檢測無酒精反應” [11] “經觀察未飲酒” [12] “駕駛人不明” |
$初步分析研判子類別-主要 [1] “0” [2]
“上下車輛時未注意安全” [3] “山路會車，靠山壁車未讓外緣車先行” [4]
“方向不定(不包括危險駕車)” [5] “右轉彎未依規定” [6] “左轉彎未依規定” [7]
“打瞌睡或疲勞駕駛(包括連續駕車8小時)” [8] “未依規定減速” [9]
“未保持行車安全距離” [10] “未保持行車安全間隔” [11]
“交通管制設施失靈或損毀” [12] “危險駕駛” [13] “因光線、視線遮蔽致生事故”
[14] “在道路上工作之人員未設適當標識” [15] “在道路上嬉戲或奔走不定” [16]
“有號誌路口，轉彎車未讓直行車先行” [17] “車輛未依規定暫停讓行人先行”
[18] “車輛未停妥滑動致生事故” [19] “車輛或機械操作不當(慎)” [20]
“車輛拋錨未採安全措施” [21] “車輛附屬機具或車門未盡安全措施” [22]
“車輛零件脫落” [23] “車輪脫落或輪胎爆裂” [24] “使用手持行動電話” [25]
“使用車輛自動駕駛或先進駕駛輔助系統設備(裝置)不符規定” [26]
“其他不當駕車行為” [27] “其他引起事故之疏失或行為” [28]
“其他未依規定讓車” [29] “其他交通管制不當” [30] “其他裝載不當” [31]
“其他機件失靈或故障” [32] “夜間行駛無燈光設備” [33] “尚未發現肇事因素”
[34] “爭(搶)道行駛” [35] “物品(件)滾(滑行)或飛(掉)落” [36]
“恍神、緊張、心不在焉分心駕駛” [37]
“施工安全防護措施未依規定或未盡完善(備)” [38]
“相關跡證不足且無具體影像紀錄，當事人各執一詞，經分析後無法釐清肇事原因”
[39] “乘客、車上動(生)物干擾分心駕駛” [40] “倒車未依規定” [41]
“峻狹坡路會車，下坡車未讓上坡車先行” [42] “起步時未注意安全” [43]
“逆向行駛” [44] “酒醉(後)駕駛” [45] “閃避不當(慎)” [46]
“停車操作時未注意安全” [47] “動物竄出” [48] “強風、暴雨、濃霧(煙)” [49]
“患病或服用藥物(疲勞)駕駛” [50] “被車輛輾壓之不明物體彈飛” [51]
“無號誌路口，少線道未讓多線道先行” [52]
“無號誌路口，支線道未讓幹線道先行” [53]
“無號誌路口，轉彎車未讓直行車先行” [54] “超速駕駛” [55]
“開啟或關閉車門不當” [56] “飲食、抽(點)菸、拿(撿)物品分心駕駛” [57]
“煞車失靈或故障” [58] “裝卸貨物不當” [59] “裝載未盡安全措施” [60]
“裝載貨物不穩妥” [61] “載運貨物超重” [62]
“道路設施(備)、植栽或其他裝置，倒塌或掉(斷)落” [63]
“違反其他標誌(線)禁制” [64] “違反禁止進入標誌” [65]
“違反禁止變換車道標線” [66] “違反禁行車種標誌(字)” [67]
“違反遵行方向標誌(線)” [68] “違規(臨時)停車” [69] “違規超車” [70]
“肇事逃逸未查獲，無法查明肇因” [71] “操作、觀看行車輔助或娛樂性顯示設備”
[72] “變換車道不當” [73]
“觀看其他事故、活動、道路環境或車外資訊分心駕駛” |
$肇事逃逸(是否肇逃) [1] “0” “否” “是”
``` |
r summary(A1A2A3_113_1_2.unique) |
Length Class Mode 年 1 -none- numeric 月 2 -none- numeric 日 31 -none- numeric 時 24 -none- character 分 60 -none- character 秒 60 -none- character 事故類別 3 -none- character 縣市 17 -none- character 市區鄉鎮 165 -none- character 路線 10 -none- character 公里 418 -none- numeric 公尺 19 -none- numeric 里程 2547 -none- numeric 分局 3 -none- character 工務段 14 -none- character 向 6 -none- character 車道線(側)名稱 198 -none- character 24小時內死亡人數 2 -none- numeric 2-30日內死亡人數 2 -none- numeric 受傷 10 -none- numeric 天候 7 -none- character 道路照明設備(11207新增) 4 -none- character 道路類別 7 -none- character 速限 14 -none- numeric 道路型態 16 -none- character 事故位置 19 -none- character 路面狀況-路面鋪裝 6 -none- character 路面狀況-路面狀態 4 -none- character 路面狀況-路面缺陷 4 -none- character 道路障礙-障礙物 6 -none- character 道路障礙-視距 6 -none- character 號誌-號誌種類 5 -none- character 號誌-號誌動作 4 -none- character 車道劃分設施-分向設施 7 -none- character 車道劃分設施-分道設施-快車道或一般車道間 6 -none- character 車道劃分設施-分道設施-快慢車道間 6 -none- character 車道劃分設施-分道設施-路面邊線 3 -none- character 事故類型及型態代碼 27 -none- numeric 事故類型及型態2 25 -none- character 當事者順位 32 -none- numeric 當事者屬(性)別 5 -none- character 當事者事故發生時年齡 92 -none- numeric 受傷程度 6 -none- character 保護裝備 7 -none- character 行動電話、電腦或其他相類功能裝置名稱 6 -none- character 當事者區分(大類別) 14 -none- character 當事者區分(類別) 25 -none- character 車輛用途 6 -none- character 飲酒情形名稱 12 -none- character 初步分析研判子類別-主要 73 -none- character 肇事逃逸(是否肇逃) 3 -none- character |
r print(paste0("There have ", sum(is.na(A1A2A3_113_1_2)), " NA(s) in this dataset.")) |
[1] "There have 11836 NA(s) in this dataset." |
| # data plot |
r # all A1A2A3_113_1_2.plot <- A1A2A3_113_1_2 name <- names(A1A2A3_113_1_2.unique) for (i in 1:ncol(A1A2A3_113_1_2.plot)) { barplot(table(A1A2A3_113_1_2.plot[ ,i]), main = name[i], xlab = "類別", ylab = "頻率", las = 2) } |
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113 data classification
A1_113_1_2 <- A1A2A3_113_1_2[A1A2A3_113_1_2$事故類別 == "A1", ]
A2_113_1_2 <- A1A2A3_113_1_2[A1A2A3_113_1_2$事故類別 == "A2", ]
A3_113_1_2 <- A1A2A3_113_1_2[A1A2A3_113_1_2$事故類別 == "A3", ]| # 113 Jan. to Feb. A1 accidents (A1_113_1_2) # summary |
|---|
| # 113 Jan. to Feb. A2 accidents (A2_113_1_2) # summary |
r library(dplyr) summary(A2_113_1_2) |
| ``` 年 月 日 時 分 Min. :2024 Min. :1.000 Min. : 1.00 Length:1967 Length:1967 1st Qu.:2024 1st Qu.:1.000 1st Qu.: 9.00 Class :character Class :character Median :2024 Median :1.000 Median :14.00 Mode :character Mode :character Mean :2024 Mean :1.464 Mean :15.39 3rd Qu.:2024 3rd Qu.:2.000 3rd Qu.:23.00 Max. :2024 Max. :2.000 Max. :31.00 秒 事故類別 縣市 市區鄉鎮 路線 Length:1967 Length:1967 Length:1967 Length:1967 Length:1967 Class :character Class :character Class :character Class :character Class :character Mode :character Mode :character Mode :character Mode :character Mode :character |
| 公里 公尺 里程 分局 工務段 Min. : 0.0 Min. : 0.0 Min. : 0.2 Length:1967 Length:1967 1st Qu.: 34.0 1st Qu.:200.0 1st Qu.: 34.8 Class :character Class :character Median :104.0 Median :400.0 Median :104.2 Mode :character Mode :character Mean :151.2 Mean :425.3 Mean :151.6 3rd Qu.:268.0 3rd Qu.:700.0 3rd Qu.:268.7 Max. :426.0 Max. :900.0 Max. :426.3 向 車道線(側)名稱 24小時內死亡人數 2-30日內死亡人數 受傷 Length:1967 Length:1967 Min. :0 Min. :0.000000 Min. : 0.000 Class :character Class :character 1st Qu.:0 1st Qu.:0.000000 1st Qu.: 1.000 Mode :character Mode :character Median :0 Median :0.000000 Median : 2.000 Mean :0 Mean :0.006101 Mean : 2.146 3rd Qu.:0 3rd Qu.:0.000000 3rd Qu.: 3.000 Max. :0 Max. :1.000000 Max. :11.000 天候 道路照明設備(11207新增) 道路類別 速限 道路型態 Length:1967 Length:1967 Length:1967 Min. : 0 Length:1967 Class :character Class :character Class :character 1st Qu.: 90 Class :character Mode :character Mode :character Mode :character Median :100 Mode :character Mean : 95 3rd Qu.:110 Max. :110 事故位置 路面狀況-路面鋪裝 路面狀況-路面狀態 路面狀況-路面缺陷 道路障礙-障礙物 Length:1967 Length:1967 Length:1967 Length:1967 Length:1967 Class :character Class :character Class :character Class :character Class :character Mode :character Mode :character Mode :character Mode :character Mode :character |
| 道路障礙-視距 號誌-號誌種類 號誌-號誌動作 車道劃分設施-分向設施 Length:1967 Length:1967 Length:1967 Length:1967 Class :character Class :character Class :character Class :character Mode :character Mode :character Mode :character Mode :character |
| 車道劃分設施-分道設施-快車道或一般車道間 車道劃分設施-分道設施-快慢車道間 Length:1967 Length:1967 Class :character Class :character Mode :character Mode :character |
| 車道劃分設施-分道設施-路面邊線 事故類型及型態代碼 事故類型及型態2 當事者順位 Length:1967 Min. : 3.00 Length:1967 Min. : 1.000 Class :character 1st Qu.:13.00 Class :character 1st Qu.: 1.000 Mode :character Median :13.00 Mode :character Median : 2.000 Mean :13.87 Mean : 2.746 3rd Qu.:13.00 3rd Qu.: 4.000 Max. :30.00 Max. :12.000 當事者屬(性)別 當事者事故發生時年齡 受傷程度 保護裝備 Length:1967 Min. :-1.0 Length:1967 Length:1967 Class :character 1st Qu.:26.0 Class :character Class :character Mode :character Median :39.0 Mode :character Mode :character Mean :37.4 3rd Qu.:51.0 Max. :90.0 行動電話、電腦或其他相類功能裝置名稱 當事者區分(大類別) 當事者區分(類別) 車輛用途 Length:1967 Length:1967 Length:1967 Length:1967 Class :character Class :character Class :character Class :character Mode :character Mode :character Mode :character Mode :character |
| 飲酒情形名稱 初步分析研判子類別-主要 肇事逃逸(是否肇逃) Length:1967 Length:1967 Length:1967 Class :character Class :character Class :character Mode :character Mode :character Mode :character |
| ``` |
r n <- ncol(A2_113_1_2) A2_113_1_2.unique <- sapply(1:n, function(x){A2_113_1_2[ ,x] %>% unique() %>% unlist() %>% unname() %>% sort() %>% return()}) names(A2_113_1_2.unique) = colnames(A2_113_1_2) print(A2_113_1_2.unique) |
| ``` $年 [1] 2024 |
| $月 [1] 1 2 |
| $日 [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 |
| $時 [1] “00” “01” “02” “03” “04” “05” “06” “07” “08” “09” “10” “11” “12” “13” “14” “15” “16” “17” “18” [20] “19” “20” “21” “22” “23” |
| $分 [1] “00” “01” “02” “03” “04” “05” “06” “07” “08” “09” “10” “11” “12” “13” “14” “15” “16” “17” “18” [20] “19” “20” “21” “22” “23” “24” “25” “26” “27” “28” “29” “30” “31” “32” “33” “34” “35” “36” “37” [39] “38” “39” “40” “41” “42” “43” “44” “45” “46” “47” “48” “49” “50” “51” “52” “53” “54” “55” “56” [58] “57” “58” “59” |
| $秒 [1] “00” “01” “02” “03” “04” “05” “06” “07” “08” “09” “10” “11” “12” “13” “15” “16” “17” “18” “19” [20] “20” “21” “22” “23” “24” “25” “26” “27” “28” “30” “31” “32” “33” “34” “35” “36” “37” “38” “39” [39] “40” “41” “42” “43” “44” “45” “46” “47” “48” “49” “50” “51” “52” “53” “54” “55” “56” “57” “58” [58] “59” |
| $事故類別 [1] “A2” |
| $縣市 [1] “宜蘭縣” “南投縣” “屏東縣” “苗栗縣” “桃園市” “高雄市” “基隆市” “雲林縣” “新北市” “新竹市” “新竹縣” [12] “嘉義縣” “彰化縣” “臺中市” “臺北市” “臺南市” |
| $市區鄉鎮 [1] “七堵區” “八德區” “三民區” “三重區” “三峽區” “土城區” “大甲區” “大同區” “大肚區” “大林鎮” [11] “大社區” “大埤鄉” “大雅區” “大園區” “大溪區” “中山區” “中和區” “中寮鄉” “中壢區” “五股區” [21] “五結鄉” “仁武區” “仁德區” “內湖區” “太保市” “文山區” “斗南鎮” “水上鄉” “北區” “古坑鄉” [31] “外埔區” “左營區” “平鎮區” “民雄鄉” “永康區” “田尾鄉” “田寮區” “石碇區” “名間鄉” “后里區” [41] “安定區” “安樂區” “汐止區” “竹山鎮” “竹北市” “竹田鄉” “竹南鎮” “竹崎鄉” “西屯區” “西湖鄉” [51] “西螺鎮” “壯圍鄉” “秀水鄉” “芎林鄉” “里港鄉” “和美鎮” “坪林區” “官田區” “岡山區” “東區” [61] “松山區” “林口區” “林內鄉” “花壇鄉” “虎尾鎮” “阿蓮區” “南屯區” “南州鄉” “南投市” “南港區” [71] “後壁區” “後龍鎮” “苓雅區” “香山區” “埔里鎮” “埔鹽鄉” “桃園區” “泰山區” “神岡區” “草屯鎮” [81] “埤頭鄉” “清水區” “通霄鎮” “造橋鄉” “鹿草鄉” “麻豆區” “善化區” “湖口鄉” “新化區” “新市區” [91] “新店區” “新營區” “新豐鄉” “楊梅區” “楠梓區” “溪州鄉” “溪湖鎮” “路竹區” “彰化市” “旗山區” [101] “銅鑼鄉” “鳳山區” “潭子區” “潮州鎮” “樹林區” “橋頭區” “燕巢區” “頭份市” “頭屋鄉” “龍井區” [111] “龍崎區” “龍潭區” “龜山區” “豐原區” “關西鎮” “關廟區” “霧峰區” “寶山鄉” “蘆竹區” “鶯歌區” |
| $路線 [1] “國道10號” “國道1號” “國道2號” “國道3甲” “國道3號” “國道4號” “國道5號” “國道6號” “國道8號” |
| $公里 [1] 0 1 2 3 4 6 7 8 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 [25] 26 27 28 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 [49] 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 73 76 77 78 [73] 79 80 83 84 86 87 88 89 90 93 95 98 99 102 103 104 107 108 109 110 111 116 117 119 [97] 120 122 123 125 127 134 135 137 139 140 141 142 156 157 158 159 160 163 165 166 167 168 170 171 [121] 172 173 174 175 176 177 181 182 183 188 189 191 192 194 195 198 200 201 202 207 208 209 210 211 [145] 212 214 215 217 220 221 222 223 224 225 228 229 230 231 232 233 234 236 241 243 244 245 249 250 [169] 252 256 257 258 261 263 266 267 268 270 271 272 274 277 278 280 285 291 299 300 303 304 305 307 [193] 308 311 312 313 314 315 316 317 318 319 321 323 324 325 326 327 328 330 332 333 334 335 337 339 [217] 340 342 343 344 346 347 350 351 352 353 354 355 356 357 360 361 362 363 365 366 367 368 369 373 [241] 377 378 381 383 411 415 426 |
| $公尺 [1] 0 100 200 300 400 500 600 700 800 870 900 |
| $里程 [1] 0.20 0.40 0.50 0.90 1.10 1.70 1.90 2.20 2.30 2.80 2.90 3.10 3.20 3.30 [15] 3.40 3.50 3.70 4.00 4.30 4.60 4.80 4.90 6.00 6.40 6.50 7.20 7.30 7.40 [29] 8.00 8.10 8.20 8.50 10.20 11.00 11.10 11.30 11.40 11.50 12.00 12.40 12.60 12.70 [43] 12.80 13.40 13.70 13.80 13.90 14.00 14.40 14.60 15.00 16.00 16.90 17.30 17.40 17.70 [57] 17.80 18.00 18.30 19.00 19.10 19.20 19.30 19.40 19.70 20.30 20.50 21.30 21.60 21.90 [71] 22.00 22.30 22.40 22.50 23.20 23.70 24.00 24.70 24.80 25.30 25.50 25.70 25.80 26.10 [85] 26.40 26.70 26.80 26.87 27.00 27.10 27.30 27.50 27.70 27.80 28.00 30.50 30.70 31.20 [99] 31.60 31.90 32.10 32.30 32.60 32.80 33.00 33.30 33.50 33.60 33.80 34.10 34.20 34.30 [113] 34.60 34.70 34.80 34.90 35.00 35.60 35.70 35.80 35.90 36.00 36.30 36.90 37.00 37.40 [127] 37.50 37.70 37.80 37.90 38.00 39.00 39.20 39.30 39.40 39.60 39.70 39.80 39.90 40.40 [141] 40.50 40.80 40.90 41.20 41.30 41.60 41.70 42.00 42.20 42.30 42.50 42.70 43.20 43.30 [155] 43.80 44.00 44.30 44.40 44.80 45.40 46.00 46.70 47.20 47.80 48.40 49.00 50.20 51.00 [169] 51.50 51.70 52.00 52.40 52.90 53.90 54.00 55.00 55.80 55.90 56.30 56.50 57.10 57.30 [183] 57.50 57.90 58.30 59.60 59.90 60.00 60.80 61.30 61.80 62.40 62.50 63.00 63.80 63.90 [197] 64.20 65.70 66.00 66.20 66.50 67.80 68.00 68.60 69.80 70.40 70.80 73.00 76.00 76.30 [211] 76.40 76.70 77.00 78.10 79.00 80.70 83.00 84.20 86.00 86.50 87.50 87.80 87.90 88.00 [225] 88.30 89.10 90.00 93.40 95.00 95.20 98.20 99.50 102.10 102.40 102.50 103.00 104.20 107.00 [239] 108.90 109.50 109.60 110.70 111.30 116.90 117.90 119.60 120.70 122.20 123.80 125.90 127.50 134.80 [253] 135.00 137.30 139.60 140.00 141.90 142.90 156.30 157.80 158.00 158.20 158.40 159.00 160.00 163.90 [267] 165.40 165.70 166.40 166.80 166.90 167.50 167.90 168.00 170.70 171.40 172.20 173.60 174.70 175.60 [281] 176.30 176.90 177.20 181.00 181.20 181.90 182.00 182.20 183.40 188.70 189.10 191.00 192.50 194.10 [295] 195.50 198.00 200.20 200.70 201.70 202.00 202.80 207.50 208.40 209.00 209.30 209.50 210.00 210.20 [309] 211.00 212.00 212.60 214.00 215.20 217.20 217.60 220.20 220.70 221.50 222.40 222.80 223.70 224.00 [323] 225.90 228.00 229.60 230.40 231.70 231.90 232.70 233.10 233.20 234.30 236.10 241.20 243.00 243.10 [337] 244.30 244.50 245.00 245.90 249.10 250.20 252.60 256.50 257.50 258.10 261.20 263.30 266.80 267.00 [351] 267.90 268.10 268.30 268.70 270.10 270.60 270.80 271.10 272.80 274.00 277.70 277.90 278.50 280.00 [365] 280.20 285.60 291.50 299.20 300.50 300.70 303.60 304.00 305.80 307.60 308.00 308.30 311.10 312.00 [379] 313.10 314.20 315.20 315.60 316.20 316.30 316.40 317.00 317.60 318.70 319.20 321.00 323.50 324.20 [393] 325.40 326.60 326.70 327.80 328.20 328.60 330.50 330.70 332.90 333.40 334.40 334.50 335.20 335.40 [407] 335.90 337.60 339.90 340.80 342.40 343.40 344.50 344.60 346.00 346.40 346.50 346.60 346.90 347.30 [421] 347.40 347.50 350.70 351.50 352.10 353.00 354.40 354.50 354.60 355.00 356.60 357.00 360.20 360.90 [435] 361.70 362.70 363.20 363.80 365.10 365.20 365.30 365.40 366.20 366.30 366.70 366.80 366.90 367.00 [449] 367.50 368.80 369.10 369.20 369.30 369.90 373.60 377.70 378.80 381.00 381.10 383.70 411.20 415.00 [463] 415.70 426.30 |
| $分局 [1] “中分局” “北分局” “南分局” |
| $工務段 [1] “大甲工務段” “中壢工務段” “內湖工務段” “斗南工務段” “木柵工務段” “白河工務段” “岡山工務段” [8] “南投工務段” “屏東工務段” “苗栗工務段” “新營工務段” “頭城工務段” “關西工務段” |
| $向 [1] “北側” “西側” “東側” “南側” |
$車道線(側)名稱 [1] “中內” “中外” “中線”
“仁德系統東往北匝道” [5] “內側” “內側車道” “內側車道(高架)” “內側路肩”
[9] “王田入口匝道” “出口匝道” “出口匝道(往台64方向)” “出口匝道內側車道”
[13] “出口匝道外側車道” “外側” “外側分流到外側車道” “甲線內側車道” [17]
“交流道” “竹田系統” “服務區” “林口B出口匝道” [21] “林口集散道” “爬坡道”
“泰山轉接道內側車道” “高架北往西系統匝道” [25] “高架外側車道”
“高架高乘載車道” “高乘載車道” “高乘載車道(高架)” [29] “減速車道” “路肩”
“彰化交流道出口外側車” “輔助” [33] “輔助內側車道” “輔助外側車道”
“輔助車道往林口B” “輔助往高架” [37] “機場系統出口匝道”
“燕巢系統出口匝道” |
$24小時內死亡人數 [1] 0 |
$2-30日內死亡人數 [1] 0 1 |
| $受傷 [1] 0 1 2 3 4 5 6 7 9 11 |
| $天候 [1] “雨” “陰” “晴” |
$道路照明設備(11207新增) [1]
“有照明且開啟” “有照明未開啟或故障” “無照明” |
| $道路類別 [1] “市區道路” “村里道路” “其他” “國道” |
| $速限 [1] 0 20 25 30 40 50 60 80 90 100 110 |
| $道路型態 [1] “三岔路” “休息站或服務區” “其他” “坡路” “直路” [6] “高架道路” “隧道” “彎曲路及附近” |
| $事故位置 [1] “一般車道(未劃分快慢車道)” “加速車道” “交叉口附近” [4] “行人穿越道” “快車道” “其他” [7] “直線匝道” “減速車道” “路肩、路緣” [10] “慢車道” “環道匝道” |
$路面狀況-路面鋪裝 [1] “柏油” |
$路面狀況-路面狀態 [1] “乾燥” “濕潤” |
$路面狀況-路面缺陷 [1] “無缺陷” |
$道路障礙-障礙物 [1] “其他障礙物”
“無障礙物” “路上有停車” “道路工事(程)中” |
$道路障礙-視距 [1] “良好” “建築物”
“路上停放車輛” |
$號誌-號誌種類 [1] “行車管制號誌”
“行車管制號誌(附設行人專用號誌)” “閃光號誌” [4] “無號誌” |
$號誌-號誌動作 [1] “正常” “無動作”
“無號誌” |
$車道劃分設施-分向設施 [1] “附標記”
“窄式無柵欄” “無分向設施” “寬式(50公分以上)” |
$車道劃分設施-分道設施-快車道或一般車道間
[1] “未繪設車道線” “車道線(附標記)” “車道線(無標記)”
“禁止變換車道線(附標記)” |
$車道劃分設施-分道設施-快慢車道間 [1]
“未繪設快慢車道分隔線” “快慢車道分隔線”
“寬式快慢車道分隔島(50公分以上)” |
$車道劃分設施-分道設施-路面邊線 [1] “有”
“無” |
| $事故類型及型態代碼 [1] 3 5 6 7 9 11 12 13 17 18 19 20 21 24 25 27 28 30 |
| $事故類型及型態2 [1] “同向擦撞” “在路上作業中” “其他” “穿越道路中” [5] “追撞” “從停車後(或中)穿出” “路上翻車、摔倒” “對向擦撞” [9] “撞非固定設施” “撞建築物” “撞動物” “撞號誌、標誌桿” [13] “撞電桿” “撞護欄(樁)” “衝出路外” “衝進路中” |
| $當事者順位 [1] 1 2 3 4 5 6 7 8 9 10 11 12 |
$當事者屬(性)別 [1] “女” “男”
“無或物(動物、堆置物)” “肇事逃逸尚未查獲” |
| $當事者事故發生時年齡 [1] -1 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 [34] 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 [67] 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 84 86 88 89 90 |
| $受傷程度 [1] “2-30日內死亡” “不明” “未受傷” “受傷” |
| $保護裝備 [1] “不明” “未戴安全帽或未繫安全帶(未使用幼童安全椅)” [3] “其他(無需使用保護裝備之人)” “戴半罩式安全帽” [5] “繫安全帶(使用幼童安全椅)” |
$行動電話、電腦或其他相類功能裝置名稱 [1]
“不明” “未使用” “使用手持或有礙駕駛安全” [4] “使用免持或未有礙駕駛安全”
“非駕駛人” |
$當事者區分(大類別) [1] “人” “大客車”
“大貨車” “小客車(含客、貨兩用)” [5] “小貨車(含客、貨兩用)” “半聯結車”
“全聯結車” “曳引車” [9] “特種車” “機車” |
$當事者區分(類別) [1] “公營客運”
“民營客運” “自用” “行人” “其他人” “計程車” “乘客” [8] “租賃小貨車”
“租賃車” “救護車” “普通重型” “遊覽車” “營業用” |
| $車輛用途 [1] “其他” “非駕駛人及乘客” “砂石車” “裝載危險物品車” |
| $飲酒情形名稱 [1] “呼氣未滿0.15 mg/L或血液檢測未滿0.03%” [2] “呼氣達0.25以上未滿0.40 mg/L或血液 0.05%以上未滿0.08%” [3] “呼氣達0.55以上未滿0.80 mg/L或血液 0.11%以上未滿0.16%” [4] “非駕駛人，未檢測” [5] “無法檢測” [6] “經檢測無酒精反應” [7] “經觀察未飲酒” [8] “駕駛人不明” |
$初步分析研判子類別-主要 [1]
“方向不定(不包括危險駕車)” [2] “打瞌睡或疲勞駕駛(包括連續駕車8小時)” [3]
“未依規定減速” [4] “未保持行車安全距離” [5] “未保持行車安全間隔” [6]
“因光線、視線遮蔽致生事故” [7] “在道路上工作之人員未設適當標識” [8]
“在道路上嬉戲或奔走不定” [9] “車輛未依規定暫停讓行人先行” [10]
“車輛未停妥滑動致生事故” [11] “車輛或機械操作不當(慎)” [12]
“車輛拋錨未採安全措施” [13] “車輛零件脫落” [14] “車輪脫落或輪胎爆裂”
[15] “使用手持行動電話” [16]
“使用車輛自動駕駛或先進駕駛輔助系統設備(裝置)不符規定” [17]
“其他不當駕車行為” [18] “其他引起事故之疏失或行為” [19]
“尚未發現肇事因素” [20] “恍神、緊張、心不在焉分心駕駛” [21]
“相關跡證不足且無具體影像紀錄，當事人各執一詞，經分析後無法釐清肇事原因”
[22] “倒車未依規定” [23] “起步時未注意安全” [24] “酒醉(後)駕駛” [25]
“閃避不當(慎)” [26] “動物竄出” [27] “患病或服用藥物(疲勞)駕駛” [28]
“被車輛輾壓之不明物體彈飛” [29] “飲食、抽(點)菸、拿(撿)物品分心駕駛”
[30] “裝載未盡安全措施” [31] “裝載貨物不穩妥” [32]
“違反禁行車種標誌(字)” [33] “違規(臨時)停車” [34]
“肇事逃逸未查獲，無法查明肇因” [35] “操作、觀看行車輔助或娛樂性顯示設備”
[36] “變換車道不當” [37]
“觀看其他事故、活動、道路環境或車外資訊分心駕駛” |
$肇事逃逸(是否肇逃) [1] “否” “是” ``` |
r summary(A2_113_1_2.unique) |
Length Class Mode 年 1 -none- numeric 月 2 -none- numeric 日 31 -none- numeric 時 24 -none- character 分 60 -none- character 秒 58 -none- character 事故類別 1 -none- character 縣市 16 -none- character 市區鄉鎮 120 -none- character 路線 9 -none- character 公里 247 -none- numeric 公尺 11 -none- numeric 里程 464 -none- numeric 分局 3 -none- character 工務段 13 -none- character 向 4 -none- character 車道線(側)名稱 38 -none- character 24小時內死亡人數 1 -none- numeric 2-30日內死亡人數 2 -none- numeric 受傷 10 -none- numeric 天候 3 -none- character 道路照明設備(11207新增) 3 -none- character 道路類別 4 -none- character 速限 11 -none- numeric 道路型態 8 -none- character 事故位置 11 -none- character 路面狀況-路面鋪裝 1 -none- character 路面狀況-路面狀態 2 -none- character 路面狀況-路面缺陷 1 -none- character 道路障礙-障礙物 4 -none- character 道路障礙-視距 3 -none- character 號誌-號誌種類 4 -none- character 號誌-號誌動作 3 -none- character 車道劃分設施-分向設施 4 -none- character 車道劃分設施-分道設施-快車道或一般車道間 4 -none- character 車道劃分設施-分道設施-快慢車道間 3 -none- character 車道劃分設施-分道設施-路面邊線 2 -none- character 事故類型及型態代碼 18 -none- numeric 事故類型及型態2 16 -none- character 當事者順位 12 -none- numeric 當事者屬(性)別 4 -none- character 當事者事故發生時年齡 88 -none- numeric 受傷程度 4 -none- character 保護裝備 5 -none- character 行動電話、電腦或其他相類功能裝置名稱 5 -none- character 當事者區分(大類別) 10 -none- character 當事者區分(類別) 13 -none- character 車輛用途 4 -none- character 飲酒情形名稱 8 -none- character 初步分析研判子類別-主要 37 -none- character 肇事逃逸(是否肇逃) 2 -none- character |
r print(paste0("There have ", sum(is.na(A2_113_1_2)), " NA(s) in this dataset.")) |
[1] "There have 1367 NA(s) in this dataset." |
| # data plot |
r # all A2_113_1_2.plot <- A2_113_1_2 name <- names(A2_113_1_2.unique) for (i in 1:ncol(A2_113_1_2.plot)) { barplot(table(A2_113_1_2.plot[ ,i]), main = name[i], xlab = "類別", ylab = "頻率", las = 2) } |
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113 Jan. to Feb. A3 accidents (A3_113_1_2)
summary
library(dplyr)
summary(A3_113_1_2) 年 月 日 時 分
Min. :2024 Min. :1.000 Min. : 1.0 Length:16521 Length:16521
1st Qu.:2024 1st Qu.:1.000 1st Qu.: 8.0 Class :character Class :character
Median :2024 Median :1.000 Median :14.0 Mode :character Mode :character
Mean :2024 Mean :1.473 Mean :15.1
3rd Qu.:2024 3rd Qu.:2.000 3rd Qu.:22.0
Max. :2024 Max. :2.000 Max. :31.0
秒 事故類別 縣市 市區鄉鎮 路線
Length:16521 Length:16521 Length:16521 Length:16521 Length:16521
Class :character Class :character Class :character Class :character Class :character
Mode :character Mode :character Mode :character Mode :character Mode :character
公里 公尺 里程 分局 工務段
Min. : 0.0 Min. : 0 Min. : 0.0 Length:16521 Length:16521
1st Qu.: 37.0 1st Qu.: 0 1st Qu.: 37.4 Class :character Class :character
Median : 96.0 Median :400 Median : 96.0 Mode :character Mode :character
Mean :144.9 Mean :371 Mean :145.3
3rd Qu.:233.0 3rd Qu.:600 3rd Qu.:233.6
Max. :430.0 Max. :900 Max. :430.3
向 車道線(側)名稱 24小時內死亡人數 2-30日內死亡人數 受傷
Length:16521 Length:16521 Min. :0 Min. :0 Min. :0.0000000
Class :character Class :character 1st Qu.:0 1st Qu.:0 1st Qu.:0.0000000
Mode :character Mode :character Median :0 Median :0 Median :0.0000000
Mean :0 Mean :0 Mean :0.0001211
3rd Qu.:0 3rd Qu.:0 3rd Qu.:0.0000000
Max. :0 Max. :0 Max. :1.0000000
天候 道路照明設備(11207新增) 道路類別 速限 道路型態
Length:16521 Length:16521 Length:16521 Min. : 0.00 Length:16521
Class :character Class :character Class :character 1st Qu.: 90.00 Class :character
Mode :character Mode :character Mode :character Median :100.00 Mode :character
Mean : 87.98
3rd Qu.:110.00
Max. :110.00
事故位置 路面狀況-路面鋪裝 路面狀況-路面狀態 路面狀況-路面缺陷 道路障礙-障礙物
Length:16521 Length:16521 Length:16521 Length:16521 Length:16521
Class :character Class :character Class :character Class :character Class :character
Mode :character Mode :character Mode :character Mode :character Mode :character
道路障礙-視距 號誌-號誌種類 號誌-號誌動作 車道劃分設施-分向設施
Length:16521 Length:16521 Length:16521 Length:16521
Class :character Class :character Class :character Class :character
Mode :character Mode :character Mode :character Mode :character
車道劃分設施-分道設施-快車道或一般車道間 車道劃分設施-分道設施-快慢車道間
Length:16521 Length:16521
Class :character Class :character
Mode :character Mode :character
車道劃分設施-分道設施-路面邊線 事故類型及型態代碼 事故類型及型態2 當事者順位
Length:16521 Min. : 0.0 Length:16521 Min. : 0.000
Class :character 1st Qu.:13.0 Class :character 1st Qu.: 1.000
Mode :character Median :13.0 Mode :character Median : 2.000
Mean :14.6 Mean : 1.766
3rd Qu.:14.0 3rd Qu.: 2.000
Max. :30.0 Max. :31.000
NA's :13
當事者屬(性)別 當事者事故發生時年齡 受傷程度 保護裝備
Length:16521 Min. :-1.00 Length:16521 Length:16521
Class :character 1st Qu.:29.00 Class :character Class :character
Mode :character Median :39.00 Mode :character Mode :character
Mean :38.52
3rd Qu.:51.00
Max. :87.00
行動電話、電腦或其他相類功能裝置名稱 當事者區分(大類別) 當事者區分(類別) 車輛用途
Length:16521 Length:16521 Length:16521 Length:16521
Class :character Class :character Class :character Class :character
Mode :character Mode :character Mode :character Mode :character
飲酒情形名稱 初步分析研判子類別-主要 肇事逃逸(是否肇逃)
Length:16521 Length:16521 Length:16521
Class :character Class :character Class :character
Mode :character Mode :character Mode :character
n <- ncol(A3_113_1_2)
A3_113_1_2.unique <- sapply(1:n, function(x){A3_113_1_2[ ,x] %>% unique() %>% unlist() %>% unname() %>% sort() %>% return()})
names(A3_113_1_2.unique) = colnames(A3_113_1_2)
print(A3_113_1_2.unique)$年
[1] 2024
$月
[1] 1 2
$日
[1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31
$時
[1] "00" "01" "02" "03" "04" "05" "06" "07" "08" "09" "10" "11" "12" "13" "14" "15" "16" "17" "18"
[20] "19" "20" "21" "22" "23"
$分
[1] "00" "01" "02" "03" "04" "05" "06" "07" "08" "09" "10" "11" "12" "13" "14" "15" "16" "17" "18"
[20] "19" "20" "21" "22" "23" "24" "25" "26" "27" "28" "29" "30" "31" "32" "33" "34" "35" "36" "37"
[39] "38" "39" "40" "41" "42" "43" "44" "45" "46" "47" "48" "49" "50" "51" "52" "53" "54" "55" "56"
[58] "57" "58" "59"
$秒
[1] "00" "01" "02" "03" "04" "05" "06" "07" "08" "09" "10" "11" "12" "13" "14" "15" "16" "17" "18"
[20] "19" "20" "21" "22" "23" "24" "25" "26" "27" "28" "29" "30" "31" "32" "33" "34" "35" "36" "37"
[39] "38" "39" "40" "41" "42" "43" "44" "45" "46" "47" "48" "49" "50" "51" "52" "53" "54" "55" "56"
[58] "57" "58" "59"
$事故類別
[1] "A3"
$縣市
[1] "宜蘭縣" "南投縣" "屏東縣" "苗栗縣" "桃園市" "高雄市" "基隆市" "雲林縣" "新北市" "新竹市" "新竹縣"
[12] "嘉義市" "嘉義縣" "彰化縣" "臺中市" "臺北市" "臺南市"
$市區鄉鎮
[1] "七堵區" "九如鄉" "八德區" "三民區" "三重區" "三峽區" "三義鄉" "下營區" "土城區" "大甲區"
[11] "大同區" "大安區" "大村鄉" "大肚區" "大林鎮" "大社區" "大埤鄉" "大雅區" "大園區" "大溪區"
[21] "大寮區" "大樹區" "小港區" "中山區" "中和區" "中埔鄉" "中寮鄉" "中壢區" "五股區" "五結鄉"
[31] "仁武區" "仁德區" "內湖區" "公館鄉" "六甲區" "太保市" "文山區" "斗六市" "斗南鎮" "水上鄉"
[41] "冬山鄉" "北區" "古坑鄉" "外埔區" "左營區" "平鎮區" "民雄鄉" "永康區" "永靖鄉" "田尾鄉"
[51] "田寮區" "白河區" "石碇區" "名間鄉" "后里區" "安定區" "安南區" "安樂區" "汐止區" "竹山鎮"
[61] "竹北市" "竹田鄉" "竹東鎮" "竹南鎮" "竹崎鄉" "西屯區" "西區" "西港區" "西湖鄉" "西螺鎮"
[71] "壯圍鄉" "沙鹿區" "秀水鄉" "芎林鄉" "里港鄉" "和美鎮" "坪林區" "官田區" "宜蘭市" "岡山區"
[81] "東山區" "東區" "松山區" "林口區" "林內鄉" "林邊鄉" "芬園鄉" "花壇鄉" "虎尾鎮" "長治鄉"
[91] "阿蓮區" "前金區" "前鎮區" "南屯區" "南州鄉" "南投市" "南港區" "後壁區" "後龍鎮" "柳營區"
[101] "苑裡鎮" "苓雅區" "苗栗市" "香山區" "埔心鄉" "埔里鎮" "埔鹽鄉" "崁頂鄉" "桃園區" "泰山區"
[111] "烏日區" "神岡區" "草屯鎮" "高樹鄉" "國姓鄉" "埤頭鄉" "梓官區" "清水區" "通霄鎮" "造橋鄉"
[121] "鳥松區" "鹿草鄉" "麻豆區" "善化區" "湖口鄉" "新化區" "新市區" "新店區" "新營區" "新豐鄉"
[131] "楊梅區" "楠梓區" "溪口鄉" "溪州鄉" "溪湖鎮" "路竹區" "彰化市" "旗山區" "銅鑼鄉" "鳳山區"
[141] "潭子區" "潮州鎮" "樹林區" "橋頭區" "燕巢區" "頭份市" "頭城鎮" "頭屋鄉" "龍井區" "龍崎區"
[151] "龍潭區" "龜山區" "礁溪鄉" "豐原區" "羅東鎮" "關西鎮" "關廟區" "霧峰區" "寶山鄉" "蘆竹區"
[161] "蘇澳鎮" "鶯歌區" "麟洛鄉" "鹽水區" "鹽埔鄉"
$路線
[1] "南港連絡道線" "國道10號" "國道1號" "國道2號" "國道3甲" "國道3號"
[7] "國道4號" "國道5號" "國道6號" "國道8號"
$公里
[1] 0.0 1.0 2.0 3.0 3.6 4.0 5.0 6.0 7.0 8.0 9.0 10.0 11.0 12.0 13.0 14.0
[17] 15.0 16.0 17.0 18.0 19.0 20.0 21.0 22.0 23.0 24.0 25.0 26.0 27.0 28.0 29.0 30.0
[33] 31.0 32.0 33.0 34.0 34.2 35.0 35.7 36.0 37.0 38.0 39.0 40.0 41.0 42.0 43.0 44.0
[49] 45.0 46.0 47.0 48.0 49.0 50.0 50.3 50.8 51.0 52.0 52.4 53.0 54.0 55.0 56.0 57.0
[65] 58.0 59.0 60.0 61.0 62.0 63.0 64.0 65.0 66.0 67.0 68.0 69.0 70.0 71.0 72.0 73.0
[81] 74.0 75.0 76.0 77.0 78.0 79.0 80.0 81.0 82.0 83.0 84.0 85.0 86.0 87.0 88.0 89.0
[97] 90.0 91.0 92.0 93.0 94.0 95.0 96.0 97.0 98.0 99.0 100.0 101.0 102.0 103.0 104.0 105.0
[113] 106.0 107.0 108.0 109.0 110.0 111.0 112.0 113.0 114.0 115.0 116.0 117.0 118.0 119.0 120.0 121.0
[129] 122.0 123.0 124.0 125.0 126.0 127.0 128.0 129.0 130.0 131.0 132.0 133.0 134.0 135.0 136.0 137.0
[145] 138.0 139.0 140.0 141.0 142.0 143.0 144.0 145.0 146.0 147.0 148.0 149.0 150.0 151.0 152.0 153.0
[161] 154.0 155.0 156.0 157.0 158.0 159.0 160.0 161.0 162.0 163.0 164.0 165.0 166.0 167.0 168.0 169.0
[177] 170.0 171.0 172.0 173.0 174.0 175.0 176.0 177.0 178.0 179.0 180.0 181.0 182.0 183.0 184.0 185.0
[193] 186.0 187.0 188.0 189.0 190.0 191.0 192.0 193.0 194.0 195.0 196.0 197.0 198.0 199.0 200.0 201.0
[209] 202.0 203.0 204.0 205.0 206.0 207.0 208.0 209.0 210.0 211.0 212.0 213.0 214.0 215.0 216.0 217.0
[225] 218.0 219.0 220.0 221.0 222.0 223.0 224.0 225.0 226.0 227.0 228.0 229.0 230.0 231.0 232.0 233.0
[241] 234.0 235.0 236.0 237.0 238.0 239.0 240.0 241.0 242.0 243.0 244.0 245.0 246.0 247.0 248.0 249.0
[257] 250.0 251.0 252.0 253.0 254.0 255.0 256.0 257.0 258.0 259.0 260.0 261.0 262.0 263.0 264.0 265.0
[273] 266.0 267.0 268.0 269.0 270.0 271.0 272.0 273.0 274.0 275.0 276.0 277.0 278.0 279.0 280.0 281.0
[289] 282.0 283.0 284.0 285.0 286.0 287.0 288.0 289.0 290.0 291.0 292.0 293.0 294.0 295.0 296.0 297.0
[305] 298.0 299.0 300.0 301.0 302.0 303.0 304.0 305.0 306.0 307.0 308.0 309.0 310.0 311.0 312.0 313.0
[321] 314.0 315.0 316.0 317.0 318.0 319.0 320.0 321.0 322.0 323.0 324.0 325.0 326.0 327.0 328.0 329.0
[337] 330.0 331.0 332.0 333.0 334.0 335.0 336.0 337.0 338.0 339.0 340.0 341.0 342.0 343.0 344.0 345.0
[353] 346.0 347.0 348.0 349.0 350.0 351.0 352.0 353.0 354.0 355.0 356.0 357.0 358.0 359.0 360.0 361.0
[369] 362.0 363.0 364.0 365.0 366.0 367.0 368.0 369.0 370.0 371.0 372.0 373.0 374.0 375.0 376.0 377.0
[385] 378.0 379.0 380.0 381.0 382.0 383.0 384.0 385.0 388.0 389.0 390.0 391.0 395.0 396.0 400.0 405.0
[401] 406.0 407.0 408.0 410.0 414.0 415.0 416.0 417.0 419.0 420.0 422.0 423.0 424.0 429.0 430.0
$公尺
[1] 0 1 2 4 40 80 100 200 300 400 470 480 500 600 650 700 800 900
$里程
[1] 0.000 0.100 0.200 0.300 0.400 0.500 0.600 0.700 0.800 0.900 1.000 1.100
[13] 1.200 1.300 1.400 1.500 1.600 1.700 1.800 1.900 2.000 2.100 2.200 2.300
[25] 2.400 2.500 2.600 2.700 2.800 2.900 3.000 3.100 3.300 3.400 3.500 3.600
[37] 3.700 3.800 3.900 4.000 4.100 4.200 4.300 4.400 4.500 4.600 4.700 4.800
[49] 5.000 5.100 5.200 5.300 5.400 5.500 5.600 5.700 5.800 5.900 6.000 6.001
[61] 6.100 6.200 6.400 6.500 6.600 6.700 6.800 6.900 7.000 7.100 7.200 7.300
[73] 7.400 7.500 7.600 7.700 7.900 8.000 8.100 8.200 8.300 8.400 8.500 8.600
[85] 8.700 8.800 8.900 9.000 9.100 9.200 9.300 9.500 9.600 9.700 9.800 10.000
[97] 10.100 10.200 10.300 10.400 10.500 10.600 10.700 10.800 10.900 11.000 11.100 11.200
[109] 11.300 11.400 11.500 11.600 11.700 11.800 11.900 12.000 12.100 12.200 12.300 12.400
[121] 12.500 12.600 12.700 12.800 12.900 13.000 13.100 13.200 13.300 13.400 13.500 13.600
[133] 13.700 13.800 13.900 14.000 14.100 14.200 14.300 14.400 14.500 14.600 14.700 14.800
[145] 14.900 15.000 15.100 15.200 15.300 15.400 15.500 15.600 15.700 15.800 15.900 16.000
[157] 16.100 16.200 16.300 16.400 16.500 16.600 16.700 16.800 16.900 17.000 17.100 17.200
[169] 17.300 17.400 17.500 17.600 17.700 17.800 17.900 18.000 18.100 18.200 18.300 18.400
[181] 18.500 18.600 18.700 18.800 18.900 19.000 19.100 19.200 19.300 19.400 19.500 19.600
[193] 19.800 19.900 20.000 20.100 20.200 20.300 20.400 20.500 20.600 20.700 20.800 21.000
[205] 21.100 21.200 21.300 21.400 21.500 21.600 21.700 21.800 21.900 22.000 22.100 22.200
[217] 22.300 22.400 22.600 22.700 22.800 22.900 23.000 23.100 23.200 23.300 23.400 23.500
[229] 23.600 23.700 23.800 23.900 24.000 24.100 24.200 24.300 24.400 24.600 24.700 24.800
[241] 24.900 25.000 25.100 25.200 25.300 25.400 25.500 25.600 25.700 25.800 25.900 26.000
[253] 26.100 26.200 26.400 26.500 26.600 26.700 26.800 26.900 27.000 27.100 27.200 27.300
[265] 27.400 27.500 27.600 27.700 27.800 27.900 28.000 28.100 28.200 28.400 28.480 28.500
[277] 28.600 28.700 28.800 29.000 29.100 29.200 29.300 29.400 29.500 29.600 29.700 29.800
[289] 29.900 30.000 30.100 30.200 30.300 30.400 30.500 30.600 30.700 30.800 30.900 31.000
[301] 31.100 31.200 31.300 31.400 31.500 31.600 31.700 31.800 31.900 32.000 32.100 32.200
[313] 32.300 32.400 32.500 32.700 32.800 32.900 33.000 33.100 33.200 33.300 33.400 33.500
[325] 33.600 33.700 33.800 33.900 34.000 34.100 34.200 34.300 34.400 34.500 34.600 34.700
[337] 34.800 34.900 35.000 35.100 35.200 35.300 35.400 35.500 35.600 35.700 35.800 35.900
[349] 36.000 36.100 36.200 36.300 36.400 36.500 36.600 36.700 36.800 36.900 37.000 37.100
[361] 37.200 37.300 37.400 37.500 37.600 37.700 37.800 37.900 38.000 38.100 38.200 38.400
[373] 38.500 38.600 38.700 38.800 38.900 39.000 39.100 39.200 39.300 39.400 39.500 39.600
[385] 39.700 39.800 39.900 40.000 40.100 40.200 40.300 40.400 40.500 40.600 40.700 40.800
[397] 40.900 41.000 41.100 41.200 41.300 41.500 41.600 41.700 41.800 41.900 42.000 42.100
[409] 42.200 42.300 42.400 42.500 42.600 42.700 42.800 42.900 43.000 43.100 43.200 43.300
[421] 43.400 43.500 43.600 43.700 43.800 43.900 44.000 44.100 44.200 44.300 44.400 44.500
[433] 44.600 44.700 44.800 45.000 45.100 45.300 45.400 45.500 45.600 45.700 45.800 45.900
[445] 46.000 46.100 46.200 46.300 46.400 46.500 46.600 46.700 46.800 46.900 47.000 47.100
[457] 47.200 47.300 47.400 47.500 47.600 47.700 47.900 48.000 48.400 48.500 48.600 48.700
[469] 48.800 48.900 49.000 49.100 49.200 49.400 49.500 49.600 49.700 49.800 49.900 50.000
[481] 50.100 50.200 50.300 50.400 50.500 50.600 50.700 50.800 50.900 51.000 51.100 51.200
[493] 51.300 51.400 51.500 51.600 51.700 51.800 51.900 52.000 52.100 52.200 52.300 52.400
[505] 52.500 52.600 52.700 52.800 52.900 53.000 53.100 53.200 53.300 53.400 53.600 53.700
[517] 53.800 53.900 54.000 54.100 54.300 54.400 54.500 54.600 54.800 54.900 55.000 55.100
[529] 55.200 55.300 55.400 55.500 55.600 55.700 55.800 55.900 56.000 56.100 56.200 56.300
[541] 56.400 56.500 56.600 56.700 56.800 56.900 57.000 57.100 57.200 57.300 57.400 57.500
[553] 57.600 57.700 57.800 57.900 58.000 58.100 58.200 58.300 58.400 58.500 58.600 58.700
[565] 58.800 58.900 59.000 59.100 59.200 59.300 59.400 59.500 59.600 59.700 59.800 59.900
[577] 60.000 60.100 60.200 60.300 60.400 60.500 60.700 60.800 60.900 61.000 61.100 61.200
[589] 61.300 61.400 61.500 61.700 61.800 61.900 62.000 62.100 62.200 62.300 62.400 62.500
[601] 62.600 62.700 62.800 62.900 63.000 63.100 63.200 63.300 63.400 63.500 63.600 63.700
[613] 63.800 63.900 64.000 64.040 64.100 64.200 64.300 64.400 64.500 64.600 64.700 64.800
[625] 65.000 65.300 65.400 65.500 65.600 65.700 65.800 65.900 66.000 66.100 66.200 66.300
[637] 66.500 66.600 66.700 66.800 66.900 67.000 67.100 67.200 67.300 67.400 67.500 67.700
[649] 67.900 68.000 68.100 68.200 68.300 68.400 68.500 68.600 68.700 68.800 69.000 69.100
[661] 69.200 69.300 69.400 69.500 69.600 70.000 70.200 70.300 70.400 70.500 70.600 70.700
[673] 70.800 70.900 71.000 71.100 71.200 71.300 71.400 71.500 71.600 71.700 71.900 72.000
[685] 72.100 72.200 72.300 72.700 73.000 73.300 73.400 73.600 74.000 74.100 74.200 74.400
[697] 74.500 74.600 74.800 74.900 75.000 75.200 75.700 75.900 76.000 76.100 76.200 76.500
[709] 76.600 76.700 76.800 77.000 77.100 77.200 77.300 77.500 77.600 77.700 77.900 78.000
[721] 78.100 78.400 78.500 79.000 79.100 79.400 79.600 79.700 80.000 80.100 80.200 80.400
[733] 80.600 80.700 81.000 81.100 81.300 81.500 81.600 81.800 81.900 82.000 82.200 82.300
[745] 82.400 82.500 82.600 82.800 83.000 83.100 83.200 83.600 83.700 83.800 83.900 84.000
[757] 84.100 84.200 84.400 84.500 84.600 84.700 84.800 84.900 85.000 85.100 85.200 85.400
[769] 85.500 85.700 85.800 85.900 86.000 86.100 86.200 86.300 86.400 86.500 86.600 86.700
[781] 86.800 87.000 87.100 87.200 87.300 87.400 87.500 87.600 87.700 87.800 87.900 88.000
[793] 88.100 88.200 88.400 88.500 88.600 88.700 89.000 89.200 89.300 89.400 89.600 89.700
[805] 89.800 89.900 90.000 90.200 90.400 90.500 90.600 90.700 90.900 91.000 91.100 91.200
[817] 91.300 91.500 91.600 92.000 92.100 92.200 92.300 92.400 92.500 92.600 92.900 93.000
[829] 93.100 93.200 93.300 93.400 93.500 93.600 93.700 94.000 94.200 94.300 94.400 94.500
[841] 94.600 94.700 94.800 95.000 95.100 95.200 95.300 95.400 95.500 95.600 96.000 96.100
[853] 96.300 96.500 96.600 96.700 96.800 96.900 97.000 97.200 97.300 97.400 97.500 97.600
[865] 97.700 97.800 98.000 98.100 98.200 98.300 98.500 98.600 98.700 98.800 99.000 99.300
[877] 99.400 99.500 99.600 99.700 99.800 99.900 100.000 100.100 100.200 100.500 100.700 100.800
[889] 101.000 101.300 101.400 101.500 102.000 102.200 102.300 102.400 102.500 102.600 102.700 102.800
[901] 102.900 103.000 103.200 103.700 104.000 104.200 104.300 104.600 104.700 104.800 104.900 105.000
[913] 105.400 105.600 105.800 105.900 106.000 106.200 106.500 106.900 107.000 107.500 107.700 107.900
[925] 108.000 108.100 108.200 108.300 108.400 108.500 108.600 108.700 108.800 108.900 109.000 109.100
[937] 109.200 109.600 109.700 110.000 110.200 110.300 110.500 110.600 110.700 111.000 111.200 111.400
[949] 111.500 111.600 111.700 111.900 112.200 112.500 112.700 112.800 113.600 113.700 114.000 114.200
[961] 114.300 114.500 114.600 114.700 114.800 114.900 115.000 115.100 115.200 115.400 115.800 116.000
[973] 116.300 116.500 116.700 117.000 117.400 117.500 117.800 118.000 118.200 118.400 118.900 119.000
[985] 119.200 119.300 119.600 119.800 120.000 120.100 120.300 120.400 120.500 120.900 121.000 121.400
[997] 121.500 121.800 121.900 122.000
[ reached getOption("max.print") -- omitted 1456 entries ]
$分局
[1] "中分局" "北分局" "南分局"
$工務段
[1] "0" "大甲工務段" "中壢工務段" "內湖工務段" "斗南工務段" "木柵工務段" "白河工務段"
[8] "岡山工務段" "南投工務段" "屏東工務段" "苗栗工務段" "新營工務段" "頭城工務段" "關西工務段"
$向
[1] "口" "北側" "西側" "東側" "附近" "南側"
$`車道線(側)名稱`
[1] "F15停車格前" "九如入口匝道" "九如南向出口匝道" "入口匝道"
[5] "入口匝道內側" "入口匝道內側車道" "入口匝道外側車道" "入口環道"
[9] "三重出口匝道內側車道" "下營系統西往東匝道" "土城出口匝道中線車道" "大竹入口匝道"
[13] "大竹入口匝道(青埔方" "大車專用車道\t內側車" "中內" "中內車道"
[17] "中外" "中正入口匝道" "中線" "中線車道"
[21] "中線車道(桃園小直線)" "中線車道(高架)" "中壢入口匝道" "中壢服務區"
[25] "中壢服務區(內側車道)" "中壢服務區C區停車區" "中壢服務區E區停車場" "中壢服務區內側車道"
[29] "中壢服務區出口匝道" "中壢服務區外圍道路內" "中壢服務區停車場C區" "五堵入口匝道"
[33] "五楊高架-內側車道" "五楊高架內側車道" "五楊高架外側車道" "仁武交流道出口匝道"
[37] "仁德B出口匝道" "仁德入口匝道" "仁德系統入口匝道" "內側"
[41] "內側入口匝道" "內側車道" "內側車道(分流車道)" "內側車道(高架)"
[45] "內側路肩" "內壢出口匝道" "內壢出口匝道(大園方" "內壢出口匝道外側車道"
[49] "王田入口匝道" "出口匝道" "出口匝道-外側車道" "出口匝道\t外側車道"
[53] "出口匝道(五股交流道" "出口匝道(往青埔方向)" "出口匝道(往新屋方向)" "出口匝道(南往東)"
[57] "出口匝道中線車道" "出口匝道內側車道" "出口匝道外側車道" "出口匝道外側車道(中"
[61] "出口匝道往五股" "出口匝道往蓬萊路" "出口外側" "出口專用車道"
[65] "出口專用道" "加油站" "加速車道" "加速車道(內側)"
[69] "北斗入口匝道" "北向豐原入口匝道" "北往西匝道" "匝道"
[73] "匝道內側" "匝道出口" "台74線接國3入口匝道" "台88匯國一匝道"
[77] "台中系統匝道" "右轉專用道" "外側" "外側分流道外側車道"
[81] "外側車道" "外側車道(外側分流道)" "外側車道(林口小直線)" "外側車道(高架)"
[85] "外側開放路肩" "外側路肩" "外側輔助車道" "外側護欄"
[89] "左側出口匝道(民雄交" "平鎮系統匝道" "甲線出口匝道內側車道" "交流道"
[93] "休息站" "地磅站車道" "圳頭出口匝道外側車道" "汐止系統出口匝道"
[97] "竹山入口匝道" "竹山出口匝道" "西螺服務區" "系統匝道(西往南)"
[101] "岡山入口匝道" "往官田系統出口匝道" "服務區" "東往南匝道(高架方向)"
[105] "林口A入口匝道" "林口A出口匝道內側車" "林口A出口匝道外側" "林口A出口匝道外側車"
[109] "林口B入口匝道" "林口B出口匝道中線車" "林口B出口匝道內側車" "林口B出口匝道外側車"
[113] "爬坡" "直線匝道" "虎尾交流道出口匝道" "便道"
[117] "南屯出口匝道外側車道" "南往東匝道" "桃園A出口匝道中線車" "桃園A出口匝道外側車"
[121] "桃園B出口匝道-外側車" "桃園入口匝道內側車道" "泰山轉接道" "泰山轉接道內側車道"
[125] "泰山轉機道內側車道" "泰安服務區" "草屯出口匝道" "高架HOV車道"
[129] "高架中線車道" "高架內側內側" "高架內側車道" "高架出口匝道外側車道"
[133] "高架北向內側車道" "高架外側" "高架外側車道" "高架泰山轉接道內側車"
[137] "高架高乘載車道" "高架專用道專用道" "高架輔助車道" "高架轉接道內側"
[141] "高乘載車道" "國二甲線外側車道" "國八往國三加速車道" "減速車道"
[145] "開放時段外側路肩" "圓山入口匝道內側車道" "新化休息站小型車停車" "新台五出口匝道"
[149] "新營出口匝道" "楊梅出口匝道內側車道" "楠梓北向出口匝道" "楠梓南入匝道"
[153] "路口" "路竹西往北匝道" "路肩" "路肩開放之外側路肩"
[157] "彰化入口匝道" "彰化系統出口" "輔助" "輔助內側"
[161] "輔助內側車道" "輔助內側車道往五股方" "輔助外側" "輔助外側車道"
[165] "輔助外側車道往五股方" "輔助車道" "輔助車道外側車道" "輔助車道往五股"
[169] "輔助車道往五楊高架" "輔助車道往桃園A" "輔助車道往高架" "輔助車道往高架方向"
[173] "輔助往高架" "機場系統入口匝道內側" "機場系統入口匝道外側" "機場系統西往北匝道"
[177] "機場系統南往東匝道" "環河北路出口匝道外側" "豐原交流道出口匝道" "豐勢出口匝道外側車道"
[181] "邊坡" "關廟服務區小型車D區" "霧峰出口匝道" "寶山匝道入口"
$`24小時內死亡人數`
[1] 0
$`2-30日內死亡人數`
[1] 0
$受傷
[1] 0 1
$天候
[1] "0" "雨" "風" "風沙" "陰" "晴" "霧或煙"
$`道路照明設備(11207新增)`
[1] "0" "有照明且開啟" "有照明未開啟或故障" "無照明"
$道路類別
[1] "0" "快速(公)道" "村里道路" "其他" "國道" "專用道路"
$速限
[1] 0 10 20 25 30 40 50 60 70 80 90 100 101 110
$道路型態
[1] "0" "三岔路" "四岔路" "休息站或服務區" "地下道"
[6] "多岔路" "其他" "坡路" "直路" "高架道路"
[11] "無遮斷器" "圓環" "廣場" "橋樑" "隧道"
[16] "彎曲路及附近"
$事故位置
[1] "0" "一般車道(未劃分快慢車道)" "加速車道"
[4] "交叉口附近" "交叉路口內" "交通島(含槽化線)"
[7] "自行車專用道" "快車道" "其他"
[10] "直路" "直線匝道" "迴轉道"
[13] "高架道路" "減速車道" "路肩、路緣"
[16] "慢車道" "隧道" "環道匝道"
$`路面狀況-路面鋪裝`
[1] "0" "水泥" "快車道" "其他鋪裝" "柏油" "無鋪裝"
$`路面狀況-路面狀態`
[1] "0" "冰雪" "乾燥" "濕潤"
$`路面狀況-路面缺陷`
[1] "0" "突出(高低)不平" "無缺陷" "路面鬆軟"
$`道路障礙-障礙物`
[1] "0" "有堆積物" "其他障礙物" "無障礙物" "路上有停車"
[6] "道路工事(程)中"
$`道路障礙-視距`
[1] "0" "良好" "建築物" "路上停放車輛" "樹木、農作物" "彎道"
$`號誌-號誌種類`
[1] "0" "行車管制號誌" "行車管制號誌(附設行人專用號誌)"
[4] "閃光號誌" "無號誌"
$`號誌-號誌動作`
[1] "0" "正常" "無動作" "無號誌"
$`車道劃分設施-分向設施`
[1] "0" "附標記" "窄式附柵欄" "窄式無柵欄" "無分向設施"
[6] "無標記" "寬式(50公分以上)"
$`車道劃分設施-分道設施-快車道或一般車道間`
[1] "0" "未繪設車道線" "車道線(附標記)" "車道線(無標記)"
[5] "禁止變換車道線(附標記)" "禁止變換車道線(無標記)"
$`車道劃分設施-分道設施-快慢車道間`
[1] "0" "未繪設快慢車道分隔線" "快慢車道分隔線"
[4] "窄式快慢車道分隔島(附柵欄)" "窄式快慢車道分隔島(無柵欄)" "寬式快慢車道分隔島(50公分以上)"
$`車道劃分設施-分道設施-路面邊線`
[1] "0" "有" "無"
$事故類型及型態代碼
[1] 0 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 28 29 30
$事故類型及型態2
[1] "0" "同向擦撞" "其他" "倒車撞" "追撞"
[6] "側撞" "路上翻車、摔倒" "路口交岔撞" "對向擦撞" "對撞"
[11] "撞工程施工" "撞交通島" "撞非固定設施" "撞建築物" "撞動物"
[16] "撞號誌、標誌桿" "撞路樹" "撞橋樑(橋墩)" "撞護欄(樁)" "衝出路外"
$當事者順位
[1] 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31
$`當事者屬(性)別`
[1] "0" "女" "男" "無或物(動物、堆置物)"
[5] "肇事逃逸尚未查獲"
$當事者事故發生時年齡
[1] -1 0 3 7 9 11 15 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42
[34] 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75
[67] 76 77 78 79 80 81 82 83 84 85 87
$受傷程度
[1] "0" "不明" "未受傷" "受傷"
$保護裝備
[1] "0" "不明"
[3] "未戴安全帽或未繫安全帶(未使用幼童安全椅)" "其他(無需使用保護裝備之人)"
[5] "戴半罩式安全帽" "戴非半罩式安全帽"
[7] "繫安全帶(使用幼童安全椅)"
$`行動電話、電腦或其他相類功能裝置名稱`
[1] "0" "不明" "未使用"
[4] "使用手持或有礙駕駛安全" "使用免持或未有礙駕駛安全" "非駕駛人"
$`當事者區分(大類別)`
[1] "0" "人" "大客車" "大貨車"
[5] "小客車(含客、貨兩用)" "小貨車(含客、貨兩用)" "半聯結車" "全聯結車"
[9] "曳引車" "其他車" "軍車" "特種車"
[13] "慢車" "機車"
$`當事者區分(類別)`
[1] "0" "大型重型2(250-550C.C.)" "小型車"
[4] "公營公車" "公營客運" "民營公車"
[7] "民營客運" "自用" "自用大客車"
[10] "其他人" "其他車" "其他特種車"
[13] "計程車" "乘客" "租賃小貨車"
[16] "租賃車" "動力機械" "救護車"
[19] "普通重型" "微型電動二輪車" "載重車"
[22] "遊覽車" "營業用" "警備車"
$車輛用途
[1] "0" "幼童車" "其他" "非駕駛人及乘客" "砂石車"
[6] "裝載危險物品車"
$飲酒情形名稱
[1] "0"
[2] "呼氣未滿0.15 mg/L或血液檢測未滿0.03%"
[3] "呼氣達0.15以上未滿0.25 mg/L或血液 0.03%以上未滿0.05%"
[4] "呼氣達0.25以上未滿0.40 mg/L或血液 0.05%以上未滿0.08%"
[5] "呼氣達0.4以上未滿0.55mg/L或血液達 0.08%以上未滿0.11%"
[6] "呼氣達0.55以上未滿0.80 mg/L或血液 0.11%以上未滿0.16%"
[7] "呼氣檢測 0.80 mg/L以上或血液檢測 0.16%以上"
[8] "非駕駛人，未檢測"
[9] "無法檢測"
[10] "經檢測無酒精反應"
[11] "經觀察未飲酒"
[12] "駕駛人不明"
$`初步分析研判子類別-主要`
[1] "0"
[2] "上下車輛時未注意安全"
[3] "山路會車，靠山壁車未讓外緣車先行"
[4] "方向不定(不包括危險駕車)"
[5] "右轉彎未依規定"
[6] "左轉彎未依規定"
[7] "打瞌睡或疲勞駕駛(包括連續駕車8小時)"
[8] "未依規定減速"
[9] "未保持行車安全距離"
[10] "未保持行車安全間隔"
[11] "交通管制設施失靈或損毀"
[12] "危險駕駛"
[13] "因光線、視線遮蔽致生事故"
[14] "有號誌路口，轉彎車未讓直行車先行"
[15] "車輛未停妥滑動致生事故"
[16] "車輛或機械操作不當(慎)"
[17] "車輛拋錨未採安全措施"
[18] "車輛附屬機具或車門未盡安全措施"
[19] "車輛零件脫落"
[20] "車輪脫落或輪胎爆裂"
[21] "使用手持行動電話"
[22] "使用車輛自動駕駛或先進駕駛輔助系統設備(裝置)不符規定"
[23] "其他不當駕車行為"
[24] "其他引起事故之疏失或行為"
[25] "其他未依規定讓車"
[26] "其他交通管制不當"
[27] "其他裝載不當"
[28] "其他機件失靈或故障"
[29] "夜間行駛無燈光設備"
[30] "尚未發現肇事因素"
[31] "爭(搶)道行駛"
[32] "物品(件)滾(滑行)或飛(掉)落"
[33] "恍神、緊張、心不在焉分心駕駛"
[34] "施工安全防護措施未依規定或未盡完善(備)"
[35] "相關跡證不足且無具體影像紀錄，當事人各執一詞，經分析後無法釐清肇事原因"
[36] "乘客、車上動(生)物干擾分心駕駛"
[37] "倒車未依規定"
[38] "峻狹坡路會車，下坡車未讓上坡車先行"
[39] "起步時未注意安全"
[40] "逆向行駛"
[41] "酒醉(後)駕駛"
[42] "閃避不當(慎)"
[43] "停車操作時未注意安全"
[44] "動物竄出"
[45] "強風、暴雨、濃霧(煙)"
[46] "患病或服用藥物(疲勞)駕駛"
[47] "被車輛輾壓之不明物體彈飛"
[48] "無號誌路口，少線道未讓多線道先行"
[49] "無號誌路口，支線道未讓幹線道先行"
[50] "無號誌路口，轉彎車未讓直行車先行"
[51] "超速駕駛"
[52] "開啟或關閉車門不當"
[53] "飲食、抽(點)菸、拿(撿)物品分心駕駛"
[54] "煞車失靈或故障"
[55] "裝卸貨物不當"
[56] "裝載未盡安全措施"
[57] "裝載貨物不穩妥"
[58] "載運貨物超重"
[59] "道路設施(備)、植栽或其他裝置，倒塌或掉(斷)落"
[60] "違反其他標誌(線)禁制"
[61] "違反禁止進入標誌"
[62] "違反禁止變換車道標線"
[63] "違反遵行方向標誌(線)"
[64] "違規(臨時)停車"
[65] "違規超車"
[66] "肇事逃逸未查獲，無法查明肇因"
[67] "操作、觀看行車輔助或娛樂性顯示設備"
[68] "變換車道不當"
[69] "觀看其他事故、活動、道路環境或車外資訊分心駕駛"
$`肇事逃逸(是否肇逃)`
[1] "0" "否" "是"summary(A3_113_1_2.unique) Length Class Mode
年 1 -none- numeric
月 2 -none- numeric
日 31 -none- numeric
時 24 -none- character
分 60 -none- character
秒 60 -none- character
事故類別 1 -none- character
縣市 17 -none- character
市區鄉鎮 165 -none- character
路線 10 -none- character
公里 415 -none- numeric
公尺 18 -none- numeric
里程 2456 -none- numeric
分局 3 -none- character
工務段 14 -none- character
向 6 -none- character
車道線(側)名稱 184 -none- character
24小時內死亡人數 1 -none- numeric
2-30日內死亡人數 1 -none- numeric
受傷 2 -none- numeric
天候 7 -none- character
道路照明設備(11207新增) 4 -none- character
道路類別 6 -none- character
速限 14 -none- numeric
道路型態 16 -none- character
事故位置 18 -none- character
路面狀況-路面鋪裝 6 -none- character
路面狀況-路面狀態 4 -none- character
路面狀況-路面缺陷 4 -none- character
道路障礙-障礙物 6 -none- character
道路障礙-視距 6 -none- character
號誌-號誌種類 5 -none- character
號誌-號誌動作 4 -none- character
車道劃分設施-分向設施 7 -none- character
車道劃分設施-分道設施-快車道或一般車道間 6 -none- character
車道劃分設施-分道設施-快慢車道間 6 -none- character
車道劃分設施-分道設施-路面邊線 3 -none- character
事故類型及型態代碼 22 -none- numeric
事故類型及型態2 20 -none- character
當事者順位 32 -none- numeric
當事者屬(性)別 5 -none- character
當事者事故發生時年齡 77 -none- numeric
受傷程度 4 -none- character
保護裝備 7 -none- character
行動電話、電腦或其他相類功能裝置名稱 6 -none- character
當事者區分(大類別) 14 -none- character
當事者區分(類別) 24 -none- character
車輛用途 6 -none- character
飲酒情形名稱 12 -none- character
初步分析研判子類別-主要 69 -none- character
肇事逃逸(是否肇逃) 3 -none- characterprint(paste0("There have ", sum(is.na(A3_113_1_2)), " NA(s) in this dataset."))[1] "There have 10397 NA(s) in this dataset."data plot
# all
A3_113_1_2.plot <- A3_113_1_2
name <- names(A3_113_1_2.unique)
for (i in 1:ncol(A3_113_1_2.plot)) {
barplot(table(A3_113_1_2.plot[ ,i]), main = name[i], xlab = "類別", ylab = "頻率", las = 2)
}
國道1號
# 拆分原資料
highway_1_112_1_10 <- A1A2A3_112_1_10[A1A2A3_112_1_10$路線 == "國道1號", ]
highway_1_112_1_10$時 <- as.integer(highway_1_112_1_10$時)
highway_1_112_1_10$公里 <- as.integer(highway_1_112_1_10$公里)
highway_1_112_1_10 <- highway_1_112_1_10[!is.na(highway_1_112_1_10$時), ]
highway_1_112_1_10 <- highway_1_112_1_10[!is.na(highway_1_112_1_10$公里), ]
print(highway_1_112_1_10)# 製作國道每25公里每小時事故量表格
per_km <- 25
name <- ((1:round(374.3 / per_km)) - 1) * per_km
highway_1_accidents_per_25km <- data.frame(matrix(data = 0, nrow = length(name), ncol = 24))
rownames(highway_1_accidents_per_25km) <- name
colnames(highway_1_accidents_per_25km) <- paste0("hour.", 0:23)
for (i in 1:nrow(highway_1_112_1_10)) {
km <- (sort(c(name, highway_1_112_1_10$公里[i])) == highway_1_112_1_10$公里[i])[-1]
highway_1_accidents_per_25km[km, (highway_1_112_1_10$時[i] + 1)] = highway_1_accidents_per_25km[km, (highway_1_112_1_10$時[i] + 1)] + 1
}
print(highway_1_accidents_per_25km)# plot per 25 km per hour
plot_25km <- t(highway_1_accidents_per_25km)
for (i in 1:ncol(plot_25km)) {
barplot(plot_25km[ ,i], main = paste0(colnames(plot_25km)[i], "km"), xlab = "時間", ylab = "頻率", las = 2)
}
# 製作國道每1公里每小時事故量表格
per_km <- 1
name <- ((1:round(374.3 / per_km)) - 1) * per_km
highway_1_accidents_per_1km <- data.frame(matrix(data = 0, nrow = length(name), ncol = 24))
rownames(highway_1_accidents_per_1km) <- name
colnames(highway_1_accidents_per_1km) <- paste0("hour.", 0:23)
for (i in 1:nrow(highway_1_112_1_10)) {
km <- (sort(c(name, highway_1_112_1_10$公里[i])) == highway_1_112_1_10$公里[i])[-1]
highway_1_accidents_per_1km[km, (highway_1_112_1_10$時[i] + 1)] = highway_1_accidents_per_1km[km, (highway_1_112_1_10$時[i] + 1)] + 1
}
print(highway_1_accidents_per_1km)# plot per 1 km per hour
plot_1km <- t(highway_1_accidents_per_1km)
for (i in 1:ncol(plot_1km)) {
png(
filename = paste0("pic/", colnames(plot_1km)[i], "km.png"), # 文件名称
width = 875, # 宽
height = 540, # 高
units = "px", # 单位
bg = "white", # 背景颜色
res = 72) # 分辨率
barplot(plot_1km[ ,i], main = paste0(colnames(plot_1km)[i], "km"), ylim = c(1, max(plot_1km)) , xlab = "時間", ylab = "頻率", las = 2)
dev.off()
}
---
title: "A1 A2 A3"
author: "Yao-Chih Hsu, "
output: html_notebook
---
# references
data download from [https://freeway2024.tw/](https://freeway2024.tw/).

# dataset input
```{r}
library(readxl)
A1_112_1_10 <- read_xlsx(path = "112年1-10月A1事故資料(113.01.12更新).xlsx")
A2_112_1_10 <- read_xlsx(path = "112年1-10月A2事故資料(113.02.06更新).xlsx")
A3_112_1_10 <- read_xlsx(path = "112年1-10月A3事故資料(113.02.06更新).xlsx")
A1A2A3_113_1_2 <- read_xlsx(path = "113年1-2月A1A2A3交通事故資料.xlsx")
```

-------------------------------------------------------------
# 112 Jan. to Oct. A1A2A3 accidents (A1A2A3_112_1_10)
# data
```{r}
A1A2A3_112_1_10 <- rbind(A1_112_1_10, A2_112_1_10, A3_112_1_10)
```

# summary
```{r}
library(dplyr)
summary(A1A2A3_112_1_10)

n <- ncol(A1A2A3_112_1_10)
A1A2A3_112_1_10.unique <- sapply(1:n, function(x){A1A2A3_112_1_10[ ,x] %>% unique() %>% unlist() %>% unname() %>% sort() %>% return()})
names(A1A2A3_112_1_10.unique) = colnames(A1A2A3_112_1_10)
print(A1A2A3_112_1_10.unique)
summary(A1A2A3_112_1_10.unique)

print(paste0("There have ", sum(is.na(A1A2A3_112_1_10)), " NA(s) in this dataset."))
```

# data plot
```{r}
# all
A1A2A3_112_1_10.plot <- A1A2A3_112_1_10
name <- names(A1A2A3_112_1_10.unique)
for (i in 1:ncol(A1A2A3_112_1_10.plot)) {
  barplot(table(A1A2A3_112_1_10.plot[ ,i]), main = name[i], xlab = "類別", ylab = "頻率", las = 2)
}
```

-------------------------------------------------------------
# 112 Jan. to Oct. A1 accidents (A1_112_1_10)
# summary
```{r}
library(dplyr)
summary(A1_112_1_10)

n <- ncol(A1_112_1_10)
A1_112_1_10.unique <- sapply(1:n, function(x){A1_112_1_10[ ,x] %>% unique() %>% unlist() %>% unname() %>% sort() %>% return()})
names(A1_112_1_10.unique) = colnames(A1_112_1_10)
print(A1_112_1_10.unique)
summary(A1_112_1_10.unique)

print(paste0("There have ", sum(is.na(A1_112_1_10)), " NA(s) in this dataset."))
```

# data plot
```{r}
# all
A1_112_1_10.plot <- A1_112_1_10
name <- names(A1_112_1_10.unique)
for (i in 1:ncol(A1_112_1_10.plot)) {
  barplot(table(A1_112_1_10.plot[ ,i]), main = name[i], xlab = "類別", ylab = "頻率", las = 2)
}
```

-------------------------------------------------------------
# 112 Jan. to Oct. A2 accidents (A2_112_1_10)
# summary
```{r}
library(dplyr)
summary(A2_112_1_10)

n <- ncol(A2_112_1_10)
A2_112_1_10.unique <- sapply(1:n, function(x){A2_112_1_10[ ,x] %>% unique() %>% unlist() %>% unname() %>% sort() %>% return()})
names(A2_112_1_10.unique) = colnames(A2_112_1_10)
print(A2_112_1_10.unique)
summary(A2_112_1_10.unique)

print(paste0("There have ", sum(is.na(A2_112_1_10)), " NA(s) in this dataset."))
```

# data plot
```{r}
# all
A2_112_1_10.plot <- A2_112_1_10
name <- names(A2_112_1_10.unique)
for (i in 1:ncol(A2_112_1_10.plot)) {
  barplot(table(A2_112_1_10.plot[ ,i]), main = name[i], xlab = "類別", ylab = "頻率", las = 2)
}
```


-------------------------------------------------------------
# 112 Jan. to Oct. A3 accidents (A3_112_1_10)
# summary
```{r}
library(dplyr)
summary(A3_112_1_10)

n <- ncol(A3_112_1_10)
A3_112_1_10.unique <- sapply(1:n, function(x){A3_112_1_10[ ,x] %>% unique() %>% unlist() %>% unname() %>% sort() %>% return()})
names(A3_112_1_10.unique) = colnames(A3_112_1_10)
print(A3_112_1_10.unique)
summary(A3_112_1_10.unique)

print(paste0("There have ", sum(is.na(A3_112_1_10)), " NA(s) in this dataset."))
```

# data plot
```{r}
# all
A3_112_1_10.plot <- A3_112_1_10
name <- names(A3_112_1_10.unique)
for (i in 1:ncol(A3_112_1_10.plot)) {
  barplot(table(A3_112_1_10.plot[ ,i]), main = name[i], xlab = "類別", ylab = "頻率", las = 2)
}
```


-------------------------------------------------------------
# 113 Jan. to Feb. A1A2A3 accidents (A1A2A3_113_1_2)
# summary
```{r}
library(dplyr)
summary(A1A2A3_113_1_2)

n <- ncol(A1A2A3_113_1_2)
A1A2A3_113_1_2.unique <- sapply(1:n, function(x){A1A2A3_113_1_2[ ,x] %>% unique() %>% unlist() %>% unname() %>% sort() %>% return()})
names(A1A2A3_113_1_2.unique) = colnames(A1A2A3_113_1_2)
print(A1A2A3_113_1_2.unique)
summary(A1A2A3_113_1_2.unique)

print(paste0("There have ", sum(is.na(A1A2A3_113_1_2)), " NA(s) in this dataset."))
```

# data plot
```{r}
# all
A1A2A3_113_1_2.plot <- A1A2A3_113_1_2
name <- names(A1A2A3_113_1_2.unique)
for (i in 1:ncol(A1A2A3_113_1_2.plot)) {
  barplot(table(A1A2A3_113_1_2.plot[ ,i]), main = name[i], xlab = "類別", ylab = "頻率", las = 2)
}
```

-------------------------------------------------------------
# 113 data classification
```{r}
A1_113_1_2 <- A1A2A3_113_1_2[A1A2A3_113_1_2$事故類別 == "A1", ]
A2_113_1_2 <- A1A2A3_113_1_2[A1A2A3_113_1_2$事故類別 == "A2", ]
A3_113_1_2 <- A1A2A3_113_1_2[A1A2A3_113_1_2$事故類別 == "A3", ]
```

-------------------------------------------------------------
# 113 Jan. to Feb. A1 accidents (A1_113_1_2)
# summary
```{r}
library(dplyr)
summary(A1_113_1_2)

n <- ncol(A1_113_1_2)
A1_113_1_2.unique <- sapply(1:n, function(x){A1_113_1_2[ ,x] %>% unique() %>% unlist() %>% unname() %>% sort() %>% return()})
names(A1_113_1_2.unique) = colnames(A1_113_1_2)
print(A1_113_1_2.unique)
summary(A1_113_1_2.unique)

print(paste0("There have ", sum(is.na(A1_113_1_2)), " NA(s) in this dataset."))
```

# data plot
```{r}
# all
A1_113_1_2.plot <- A1_113_1_2
name <- names(A1_113_1_2.unique)
for (i in 1:ncol(A1_113_1_2.plot)) {
  barplot(table(A1_113_1_2.plot[ ,i]), main = name[i], xlab = "類別", ylab = "頻率", las = 2)
}
```

-------------------------------------------------------------
# 113 Jan. to Feb. A2 accidents (A2_113_1_2)
# summary
```{r}
library(dplyr)
summary(A2_113_1_2)

n <- ncol(A2_113_1_2)
A2_113_1_2.unique <- sapply(1:n, function(x){A2_113_1_2[ ,x] %>% unique() %>% unlist() %>% unname() %>% sort() %>% return()})
names(A2_113_1_2.unique) = colnames(A2_113_1_2)
print(A2_113_1_2.unique)
summary(A2_113_1_2.unique)

print(paste0("There have ", sum(is.na(A2_113_1_2)), " NA(s) in this dataset."))
```

# data plot
```{r}
# all
A2_113_1_2.plot <- A2_113_1_2
name <- names(A2_113_1_2.unique)
for (i in 1:ncol(A2_113_1_2.plot)) {
  barplot(table(A2_113_1_2.plot[ ,i]), main = name[i], xlab = "類別", ylab = "頻率", las = 2)
}
```

-------------------------------------------------------------
# 113 Jan. to Feb. A3 accidents (A3_113_1_2)
# summary
```{r}
library(dplyr)
summary(A3_113_1_2)

n <- ncol(A3_113_1_2)
A3_113_1_2.unique <- sapply(1:n, function(x){A3_113_1_2[ ,x] %>% unique() %>% unlist() %>% unname() %>% sort() %>% return()})
names(A3_113_1_2.unique) = colnames(A3_113_1_2)
print(A3_113_1_2.unique)
summary(A3_113_1_2.unique)

print(paste0("There have ", sum(is.na(A3_113_1_2)), " NA(s) in this dataset."))
```

# data plot
```{r}
# all
A3_113_1_2.plot <- A3_113_1_2
name <- names(A3_113_1_2.unique)
for (i in 1:ncol(A3_113_1_2.plot)) {
  barplot(table(A3_113_1_2.plot[ ,i]), main = name[i], xlab = "類別", ylab = "頻率", las = 2)
}
```

------------------------------------------------------------
# 國道1號
```{r}
# 拆分原資料
highway_1_112_1_10 <- A1A2A3_112_1_10[A1A2A3_112_1_10$路線 == "國道1號", ]
highway_1_112_1_10$時 <- as.integer(highway_1_112_1_10$時)
highway_1_112_1_10$公里 <- as.integer(highway_1_112_1_10$公里)
highway_1_112_1_10 <- highway_1_112_1_10[!is.na(highway_1_112_1_10$時), ]
highway_1_112_1_10 <- highway_1_112_1_10[!is.na(highway_1_112_1_10$公里), ]
print(highway_1_112_1_10)
```

```{r}
# 製作國道每25公里每小時事故量表格
per_km <- 25
name <- ((1:round(374.3 / per_km)) - 1) * per_km
highway_1_accidents_per_25km <- data.frame(matrix(data = 0, nrow = length(name), ncol = 24))
rownames(highway_1_accidents_per_25km) <- name
colnames(highway_1_accidents_per_25km) <- paste0("hour.", 0:23)

for (i in 1:nrow(highway_1_112_1_10)) {
  km <- (sort(c(name, highway_1_112_1_10$公里[i])) == highway_1_112_1_10$公里[i])[-1]
  highway_1_accidents_per_25km[km, (highway_1_112_1_10$時[i] + 1)] = highway_1_accidents_per_25km[km, (highway_1_112_1_10$時[i] + 1)] + 1
}
print(highway_1_accidents_per_25km)
```

```{r}
# plot per 25 km per hour
plot_25km <- t(highway_1_accidents_per_25km)
for (i in 1:ncol(plot_25km)) {
  barplot(plot_25km[ ,i], main = paste0(colnames(plot_25km)[i], "km"), xlab = "時間", ylab = "頻率", las = 2)
}
```


```{r}
# 製作國道每1公里每小時事故量表格
per_km <- 1
name <- ((1:round(374.3 / per_km)) - 1) * per_km
highway_1_accidents_per_1km <- data.frame(matrix(data = 0, nrow = length(name), ncol = 24))
rownames(highway_1_accidents_per_1km) <- name
colnames(highway_1_accidents_per_1km) <- paste0("hour.", 0:23)

for (i in 1:nrow(highway_1_112_1_10)) {
  km <- (sort(c(name, highway_1_112_1_10$公里[i])) == highway_1_112_1_10$公里[i])[-1]
  highway_1_accidents_per_1km[km, (highway_1_112_1_10$時[i] + 1)] = highway_1_accidents_per_1km[km, (highway_1_112_1_10$時[i] + 1)] + 1
}
print(highway_1_accidents_per_1km)
```

```{r}
# plot per 1 km per hour

plot_1km <- t(highway_1_accidents_per_1km)
for (i in 1:ncol(plot_1km)) {
  png( 
    filename = paste0("pic/", colnames(plot_1km)[i], "km.png"), # 文件名称
    width = 875,           # 宽
    height = 540,          # 高
    units = "px",          # 单位
    bg = "white",          # 背景颜色
    res = 72)              # 分辨率
  barplot(plot_1km[ ,i], main = paste0(colnames(plot_1km)[i], "km"), ylim = c(1, max(plot_1km)) , xlab = "時間", ylab = "頻率", las = 2)
  dev.off()
  }

```











